#FederatedLearning

[cizotech]

How do you train smarter medical AI models while protecting patient data?

It’s a challenge we face every day in healthtech—and here’s how we solve it at CIZO. 👇

To boost accuracy, we augment medical image data using: ✅ Image rotation, scaling, and flipping ✅ Synthetic data generation to simulate real-world variability ✅ Deep learning for diverse, anonymized training inputs

But healthcare data demands strict privacy.

That’s why we use: 🔒 Data encryption & anonymization 🧠 Federated learning to train models without centralizing sensitive information

📱 Example: While working on apps like Medical Dicare Equipment, we protected patient privacy while improving AI models through secure, privacy-preserving data augmentation.

💡 Takeaway: Smarter training data leads to better care—only if privacy comes first.

👉 Want to build AI-powered healthcare apps that stay compliant and effective? Let’s connect.

[blue-headline]

What if your data was unhackable... even after it’s stolen? 🔐

That’s the promise of Quantum AI.

By combining federated learning with quantum encryption, your data never leaves your device—and even if someone grabs it, they can’t use it.

Quantum AI = Security by Design.

✨ Key takeaways:

Uses Quantum Key Distribution (QKD)

Incorporates differential privacy

Eliminates need for raw data transfer

Would you trust Quantum AI with your personal data? Reblog and let us know your thoughts.

🔗 Read the full post here: https://blueheadline.com/cybersecurity/your-data-is-safer-with-quantum-ai/

[govindhtech]

ASUS ESC4000-E11 Server Advances Federated AI Capabilities

Introduction

The all-purpose With its 4th generation Intel Xeon Scalable CPUs and XPUs for the Intel Data Center GPU Flex Series 170, the ASUS ESC4000-E11 server is essential for improving federated AI capabilities across a range of sectors. Because of its architecture, which maximizes distributed AI workloads, this server is perfect for industries like healthcare and finance that value privacy, scalability, and speed.

The Test Setup

Three ASUS ESC4000-E11 server computers were used to construct the testing environment. The third ASUS ESC4000-E11 was the federated server in charge of aggregating models based on the data that each federated client had, while the other two servers functioned as federated clients. Different aggregation techniques and their possible effects on the final model in federated learning settings have been the subject of many research. By default, this test combined the gradients from the federated clients using the averaging approach.

Performance insights on the federated client hardware, especially the ASUS ESC4000-E11 with the Intel Data Center GPU Flex Series 170 for acceleration, are the main goal of this test.

The following are the main metrics assessed in this setup:

Time spent training the model

Model precision

Loss of training

A thorough understanding of which hardware is most appropriate for implementing federated learning in a real-world medical setting was then provided by comparing these measures to the outcomes from Intel Xeon CPUs.

Model Inference and RA Erosion Detection

It evaluated the model’s capacity to accurately identify varying degrees of RA erosion by testing its inference performance after the federated learning procedure and the final model aggregation. Three different degrees of RA erosion severity were determined using the developed mTSS model:

Level 0: No erosion

Level 1: Light erosion

Level 2: Significant erosion

Effective treatment planning is facilitated by this categorization approach, which enables precise detection of RA development based on medical imaging.

Principal Advantages of Federated AI

Improved Data Security: By supporting federated learning, the ASUS ESC4000-E11 keeps private information dispersed. Maintaining data privacy requires this decentralization, particularly in industries with a high regulatory burden. By processing data locally, the server’s architecture protects privacy, lowers the possibility of data breaches, and guarantees compliance with strict data protection laws.

Flexibility and Scalability: As federated learning networks grow, the server’s design enables it to scale effectively. Larger datasets and more complicated models are supported by this scalability, which allows businesses to expand their AI capabilities while preserving peak performance across several edge devices or institutions.

Decreased Latency: The ASUS ESC4000-E11 reduces latency during model training and updates because to its strong processing capabilities. In real-time applications like medical diagnostics, where prompt decision-making may have a big influence on results, this latency reduction is very important.

Energy Efficiency: High performance and optimal power consumption are guaranteed by the incorporation of XPUs for the Intel Data Center GPU Flex Series 170. It is a sustainable option for extensive AI deployments because of its energy efficiency, which lowers costs and improves the environment.

Organizations may create federated learning settings that are quicker, safer, and more effective by using the ASUS ESC4000-E11, which will spur innovation in AI-driven industries.

Case Study: Federated AI in Medical Imaging Diagnostics

Multiple hospitals work together to increase the precision of AI models that identify illnesses using medical images like MRIs, CT scans, and X-rays in a real-world federated AI scenario involving medical imaging diagnostics. While developing a common AI model, each institution keeps its data locally to ensure compliance with privacy laws.

Infrastructure Setup

In order to manage demanding AI workloads and support federated learning, each hospital implements the ASUS ESC4000-E11, which is outfitted with 4th generation Intel Xeon Scalable processors and XPUs for Intel Data Center GPU Flex Series 170. The hospitals may work together with this configuration without exchanging raw data.

The Federated Learning Process

Data Preparation: To guarantee that local medical imaging data never leaves a safe environment, each institution preprocesses it internally.

Local Model Training: Hospitals train AI models on local datasets utilizing ASUS ESC4000-E11 servers, which feature XPUs for Intel Data Center GPU Flex Series 170 for faster training. In order to maintain anonymity, the training procedure stays within each hospital’s infrastructure.

Model Aggregation: A central server receives the locally trained models and aggregates them to create a global model. No raw data is shared during this aggregation procedure; only model parameters are used.

Updates to the Global Model: Each hospital receives a redistribution of the global model, which now incorporates the pooled wisdom of all hospitals. Iterations and further local training are part of the cycle.

Performance and Efficiency Gains

Faster Training Times: Hospitals can rapidly converge on a highly accurate global model thanks to the ASUS ESC4000-E11’s potent hardware, which drastically cuts down on training times.

Energy-Efficient Training: By using XPUs for Intel Data Center GPU Flex Series 170, training is carried out in an energy-efficient manner, lowering operating expenses and the environmental impact.

Improved Data Security: The sophisticated security features of fourth-generation Intel Xeon processors guarantee that patient data is safe throughout the federated learning process.

Outcome and Benefits of Medical Diagnostics

With the help of the ASUS ESC4000-E11 servers, the federated AI system produces:

An very reliable and accurate AI model that can diagnose illnesses from medical pictures A cooperative architecture that allows hospitals to get access to a variety of datasets, enhancing the generalizability of the model without jeopardizing data privacy Quicker model iterations result in the rapid deployment of diagnostic tools, which improves patient care by enabling more precise and quicker diagnoses.

Medical organizations may cooperatively improve their AI skills by using ASUS ESC4000-E11 servers in federated learning, which will improve healthcare results while maintaining data confidentiality and privacy.

In conclusion

This white paper has shown how the ASUS ESC4000-E11 AI server’s Intel Data Center GPU Flex Series 170 may greatly speed up federated learning for practical medical applications. The technology is a powerful way to advance AI in healthcare since it improves model training durations, lowers latency, and guarantees adherence to data privacy laws. By using this gear, healthcare facilities may enhance their diagnostic skills and patient care results.

Read more on Govindhtech.com

[priyarao-01]

Emerging AI Technologies Shaping the Future of Tech Companies

Generative AI: Advanced Algorithmic Content Creation

Generative AI encompasses sophisticated algorithms designed to produce new content, including text, images, music, and videos, by learning from existing data patterns. These algorithms are typically underpinned by advanced models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) . These models are meticulously trained on extensive datasets to capture the underlying structure and distribution of the data. Upon completion of training, these algorithms can generate novel, coherent outputs that closely resemble the original data. For instance, in natural language processing, models like GPT-4 can generate human-like text from a given prompt, making them invaluable in applications such as content creation, conversational agents, and automated storytelling.

In the field of visual content, generative AI is capable of creating highly realistic images or modifying existing ones, finding applications in sectors like entertainment, advertising, and design. Similarly, in the music industry, AI-driven tools can compose original pieces in diverse styles, offering unprecedented resources for musicians and producers. While generative AI opens up vast creative possibilities and enhances efficiency in various processes, it also presents challenges related to intellectual property, authenticity, and the risk of misuse, particularly in the creation of deepfakes or the propagation of automated misinformation.

Explainable AI (XAI): Enhancing Transparency in Machine Learning Models

Explainable AI (XAI) comprises a suite of methodologies aimed at demystifying the decision-making processes of AI systems, particularly those employing complex models like deep neural networks, which are often criticized as ‘black boxes.’ XAI strives to provide clarity by developing tools and techniques that offer insights into the internal workings of AI models, identifying which factors influence their decisions, and elucidating the rationale behind specific outcomes. This transparency is essential in fostering trust, especially in critical domains such as healthcare, finance, and autonomous vehicles, where the interpretability of AI-driven decisions is vital to ensure accountability and safety.

Key techniques in XAI include feature importance scoring, which identifies the most influential input variables in a model’s decision-making process, and model-agnostic approaches like Local Interpretable Model-Agnostic Explanations (LIME), which can be applied across different machine learning models to explain their predictions. By enhancing the interpretability of AI systems, XAI not only aids developers, users, and regulators in understanding and managing AI technologies but also contributes to more informed decision-making, improved model performance, and greater public acceptance of AI-driven solutions.

Federated Learning: Decentralized Machine Learning with Enhanced Privacy

Federated Learning represents a decentralized machine learning (ML) paradigm where models are trained across multiple devices or servers that retain local datasets without sharing the raw data itself. Instead, this approach distributes the training process and aggregates model updates such as weights and gradients from each device to refine a global model. This methodology enhances data privacy and security, as the data remains localized, mitigating the risk of breaches and ensuring compliance with stringent data protection regulations.

Moreover, it is particularly advantageous in scenarios involving sensitive data distributed across various locations, such as healthcare, finance, and mobile applications. For instance, in healthcare, this approach enables the development of predictive models that leverage data from multiple hospitals while safeguarding patient privacy. In the mobile domain, it allows continuous improvement of AI functionalities on devices by learning from user interactions without centralizing personal data. Federated Learning marks a significant advancement in ethical AI development, promoting both privacy preservation and robust model performance.

Reinforcement Learning (RL): Optimizing Decision-Making Through Trial and Error

Reinforcement learning (RL) is a dynamic approach to machine learning where an agent learns optimal decision-making strategies by interacting with an environment to achieve specific goals. The agent iteratively takes actions, receives feedback in the form of rewards or penalties, and adjusts its strategy to maximize cumulative rewards over time. This method is particularly effective in scenarios where the solution is not predefined and must be discovered through exploration and interaction.

RL has found widespread application across various domains, including robotics, where it enables machines to master complex tasks such as object manipulation, environmental navigation, and precision tasks. In gaming, RL has been instrumental in creating AI systems that surpass human capabilities in complex games like chess, Go, and real-time strategy games. Additionally, RL is being employed in autonomous driving, financial trading strategies, and optimizing operations in logistics and supply chain management.

Quantum AI: Leveraging Quantum Computing for Advanced AI Solutions

Quantum AI merges the transformative power of quantum computing with artificial intelligence to solve intricate problems that are beyond the reach of classical computing. Quantum computers harness quantum mechanical principles, such as superposition and entanglement, to perform computations at exponentially accelerated rates. By integrating these capabilities with AI, particularly in optimization, machine learning, and data analysis, Quantum AI holds the promise of revolutionizing industries by resolving problems that currently take years to solve in mere seconds.

In machine learning, quantum algorithms have the potential to significantly speed up model training and enhance performance by efficiently processing large datasets and complex patterns. Quantum-enhanced machine learning could optimize supply chains, financial portfolios, and drug discovery processes by simultaneously evaluating a multitude of variables and constraints. While quantum computing is still in its early stages, ongoing R&D points to groundbreaking applications, making Quantum AI a focal point for future technological breakthroughs.

AI-Driven IoT (AIoT): Enabling Intelligent, Autonomous IoT Systems

AI-Driven IoT (AIoT) represents the convergence of artificial intelligence with the Internet of Things (IoT), creating systems and devices that are smarter, more efficient, and capable of autonomous operation. By integrating AI algorithms directly into IoT networks, devices can perform real-time data analysis, make instantaneous decisions, and execute tasks independently of cloud-based systems. This integration enhances the efficiency, responsiveness, and scalability of IoT ecosystems, enabling a wide range of applications from smart homes and cities to industrial automation and healthcare.

In smart home environments, AIoT devices can learn user preferences, optimize energy consumption, and bolster security through intelligent monitoring and control systems. In industrial settings, AIoT facilitates predictive maintenance, quality assurance, and process optimization by continuously monitoring equipment and analyzing operational data. In healthcare, AIoT devices support personalized patient care, real-time monitoring, and early detection of anomalies, thereby improving patient outcomes and reducing healthcare costs. The fusion of AI and IoT is driving the next wave of technological innovation, offering unprecedented levels of automation and intelligence across various sectors.

Patent Analysis: Tracking Technological Advancements in AI and Regional Trends

Patent analysis offers a strategic lens to observe technological trends within an industry by examining the volume and nature of patents filed, as well as identifying key players. Patents provide inventors with temporary exclusive rights to their innovations, encouraging public disclosure while safeguarding intellectual property.

The countries with the highest number of AI patent publications include China, South Korea, the United States, and Taiwan, among others. It is important to note that the data from the European Patent Office reflects the location where the patent was published, which may not necessarily correspond to the nationality of the patent holder.

[scienza-magia]

Reddito passivo attraverso la potenza dell'intelligenza artificiale dello smartphone

Hyperas Chain è una blockchain innovativa che sfrutta la potenza di calcolo degli smartphone per lo sviluppo di intelligenza artificiale (AI). Attraverso il Federated Learning e l'Edge Computing, Hyperas Chain distribuisce i compiti di training dell'AI su una rete globale di dispositivi mobili, creando una piattaforma AI potente e versatile. Questo approccio decentralizzato offre numerosi vantaggi rispetto ai metodi tradizionali di training dell'AI basati su server centralizzati, tra cui: - Efficienza scalabile: Hyperas Chain può sfruttare la potenza di calcolo inutilizzata di milioni di smartphone, consentendo un training AI più rapido ed efficiente. - Sicurezza e privacy: I dati di training rimangono sui dispositivi degli utenti, riducendo i rischi di violazioni dei dati e garantendo una maggiore privacy. - Accessibilità: Chiunque possiede uno smartphone può contribuire alla rete Hyperas Chain e guadagnare ricompense in criptovaluta. Come funziona Hyperas Chain Hyperas Chain utilizza il Federated Learning per distribuire i compiti di training dell'AI su una rete di dispositivi mobili. In questo processo, un modello AI viene suddiviso in più parti e inviato a ciascun dispositivo della rete. I dispositivi eseguono quindi il training delle proprie parti del modello e restituiscono i risultati a un server centrale. Il server combina quindi i risultati individuali per aggiornare il modello AI globale. Questo approccio decentralizzato offre diversi vantaggi rispetto al training AI tradizionale basato su server centralizzati. Innanzitutto, è più scalabile, in quanto può sfruttare la potenza di calcolo inutilizzata di milioni di smartphone. In secondo luogo, è più sicuro, poiché i dati di training rimangono sui dispositivi degli utenti. Infine, è più accessibile, poiché chiunque possiede uno smartphone può contribuire alla rete. Come guadagnare con Hyperas Chain Gli utenti possono guadagnare ricompense in criptovaluta Hyperas (H2C) leasing la potenza di calcolo dei loro smartphone alla rete Hyperas Chain. La quantità di H2C guadagnata dipende da diversi fattori, tra cui la potenza di calcolo del dispositivo, la durata del tempo in cui il dispositivo è connesso alla rete e la complessità dei compiti di training AI. Gli utenti possono anche guadagnare H2C invitando altri a unirsi alla rete Hyperas Chain. Per ogni nuovo utente che si unisce utilizzando il loro codice di referral, gli utenti guadagnano una commissione sulle ricompense in H2C guadagnate dal nuovo utente. Se vuoi scoprire questa entusiasmante opportunità per guadagnare monete tramite AI Train to Earn addestrando modelli AI. Anche tu puoi partecipare all'azione e iniziare a guadagnare monete semplicemente scaricando AI Earn Hub da dal seguente link. Non dimenticare di utilizzare il mio codice di riferimento: 2a6a9b6205d4, quindi possiamo intraprendere insieme questo viaggio per guadagnare monete Conclusione Hyperas Chain è una piattaforma AI innovativa che offre un modo unico per guadagnare reddito passivo utilizzando la potenza di calcolo degli smartphone. La sua architettura decentralizzata offre numerosi vantaggi rispetto ai metodi tradizionali di training dell'AI, tra cui scalabilità, sicurezza e accessibilità. Se sei interessato a saperne di più su Hyperas Chain o su come unirti alla rete, visita il sito web ufficiale. Elaborato con Gemini di Google Read the full article

[yashmedica]

Big Data in Healthcare: Transforming Medicine with Federated Learning and Real-World Data

Big Data in Healthcare – Revolutionizing Medicine with Federated Learning The healthcare industry is swimming in a sea of data. Electronic health records, medical imaging, genomic data – the volume, variety, and velocity of this information is immense. This is where Big Data in Healthcare comes in, offering the potential to transform how we diagnose, treat, and prevent diseases. One exciting…

[sameke1-blog]

Unveiling Tomorrow: A Deep Dive into the Latest AI Innovations Shaping the Tech Landscape

[mywordsolutionedu-blog]

📚💡 Struggling with your DATA4300 Assignment? We've got your back! 💡📚

Need help understanding Data Governance, Privacy, or Federated Learning for your DATA4300: Data Security and Ethics assignment? 🧠✨

We provide expert guidance on:

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Whether you’re stuck on defining governance frameworks or evaluating ethical implications of federated systems — we can help you submit with confidence! 💼📈

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#DataSecurity #FederatedLearning #PrivacyMatters #AssignmentHelp #DATA4300 #DataGovernance #EthicalAI #UniHelp #StudySmart #KBS

[guillaumelauzier]

Managing the Supply Chain for Tokenized Generative Assets

Generative assets supply chain traceability is an important concept that aims to ensure transparency in the creation, distribution, and sale of tokenized generative assets. Tokenized generative assets involve the use of data from various sources such as social media data, weather data, or financial data to generate artwork using generative algorithms. These digital artworks are then converted into non-fungible tokens (NFTs) that serve as a digital certificate of ownership for the artwork. The NFTs can be bought, sold, and traded on various online marketplaces.

Supply chain of generative assets

The supply chain of tokenized generative assets involves several steps, including data extraction, data providers, generative algorithms, creation, tokenization, listing, sale, storage and delivery, and royalty payments. The data extraction step involves collecting data from various sources, which is then used as input for the generative algorithm. The data providers are the companies or organizations that provide the data, such as weather services or financial institutions. The generative algorithm is the computer program or algorithm that uses the input data to generate the artwork. The creation step involves the actual creation of the generative artwork using the algorithm, and the digital artwork is stored on a computer or server. The tokenization step involves converting the digital artwork into a non-fungible token (NFT), which serves as a digital certificate of ownership for the artwork. The listing step involves listing the NFT for sale on various online marketplaces, such as OpenSea or Nifty Gateway. The sale step involves the purchase of the NFT by a buyer, who receives the digital certificate of ownership for the artwork. The storage and delivery step involves the storage of the digital artwork and the NFT in a digital wallet, which can be accessed by the owner using a private key. Finally, the royalty payments step involves paying the original artist a percentage of the sale price as a royalty payment when the artwork is sold again in the future. To ensure transparency in the supply chain of tokenized generative assets, various smart contracts have been developed. For example, the dataprovider.sol contract allows authorized data providers to provide geographic data of food supply by calling the provideData function and sending the specified amount of ether as payment. The generativeartist.sol contract allows the creation of ERC721 tokens that represent generative art maps based on geographic data of food supply. The royalties.sol contract implements the IERC2981 interface, which defines a standard way to retrieve information about royalty payments for a given NFT. The supplychain.sol contract extends the Royalties contract to handle the distribution of royalties to the data provider and the generative artist for a tokenized generative artwork. The aimodels.sol contract added a new property to the GenerativeArt contract, which is the address of an AI model contract. When a transfer occurs, the royalties are distributed to the data provider, generative artist, and the AI model contract. Finally, the global_model.cpp implementation of the GlobalModel class and the local_model.cpp implementation of the LocalModel class are used in federated learning systems to ensure transparency and accountability in the training of machine learning models.

Conclusion

The traceability of the supply chain of tokenized generative assets is an important concept that ensures transparency and accountability in the creation, distribution, and sale of these digital assets. Various smart contracts and machine learning models have been developed to achieve this goal, and these tools can help to support a more transparent and equitable ecosystem for tokenized generative assets. See Github repo on Managing the Supply Chain for Tokenized Generative Assets Read the full article

[reportwire]

Rhino Health Platform Powers Hospital-Based Federated Learning Consortium

Rhino Health Platform Powers Hospital-Based Federated Learning Consortium

Healthcare Institutions Around the Globe Collaborate with Disparate Data Securely to Transform Healthcare AI Development and Clinical Translation Press Release – May 5, 2022 BOSTON, May 5, 2022 (Newswire.com) – Rhino Health, a distributed compute platform leveraging the privacy-preserving federated learning concept, today announced a hospital-based federated learning for medicine…

[surveycircle]

Tweeted

Participants needed for online survey! Topic: "Customer preferences on personalization and privacy" https://t.co/3Q3EBwiSkA via @SurveyCircle #customers #personalization #privacy #FederatedLearning #DifferentialPrivacy #survey #surveycircle https://t.co/rXpIVb8lfd

— Daily Research @SurveyCircle (@daily_research) Mar 19, 2023

[govindhtech]

Upcoming Developments In Federated Learning AI Technologies

What is Federated learning?

Federated learning AI provides a means of unlocking information to feed new AI applications while training AI models without anybody seeing or touching your data.

The recommendation engines, chatbots, and spam filters that have made artificial intelligence a commonplace in contemporary life were developed using data mountains of training samples that were either scraped from the internet or supplied by users in return for free music, email, and other benefits.

A large number of these AI programs were trained using data that was collected and processed in one location. However, modern AI is moving in the direction of a decentralized strategy. Collaboratively, new AI models are being trained on the edge using data that never leaves your laptop, private server, or mobile device.

Federated learning AI model is a new kind of AI training that is quickly becoming the norm for processing and storing private data in order to comply with a number of new requirements. Federated learning also provides a means of accessing the raw data coming from sensors on satellites, bridges, factories, and an increasing number of smart gadgets on our bodies and in our homes by processing data at its source.

IBM is co-organizing a federated learning session at this year’s NeurIPS, the premier machine learning conference in the world, to foster conversation and idea sharing for developing this emerging subject.

How Federated Learning AI Model Works?

Similar to a team report or presentation, federated learning allows many individuals to remotely share their data in order to jointly train a single deep learning model and improve incrementally. The model, often a pre-trained foundation model, is downloaded by each participant from a cloud datacenter.

After training it on their personal information, they condense and encrypt the updated model configuration. After being decrypted and averaged, the model updates are returned to the cloud and incorporated into the centralized model. The collaborative training process keeps going iteration after iteration until the model is completely trained.

There are three variations of this decentralized, dispersed training method. Similar datasets are used to train the central model in horizontal federated learning. The data are complimentary in vertical federated learning; for instance, a person’s musical interests may be predicted by combining their assessments of books and movies.

Lastly, in federated transfer learning, a foundation model that has already been trained to do one task such as recognizing cars is trained on a different dataset to accomplish another such as identifying cats. The integration of foundation models into federated learning is now being worked on by Baracaldo and her colleagues. One possible use case is for banks to build an AI model to identify fraud and then repurpose it for other purposes.

Advantages Of Federated Learning

Federated learning AI model has a number of clear benefits, particularly where decentralized data processing and data privacy are crucial. Here are a few main benefits:

Improved Privacy of Data

By enabling model training on decentralized data sources without direct access to the raw data, federated learning puts privacy first. By ensuring that private or sensitive data stays on local devices, this decentralized method lowers the possibility of data breaches.

Enhanced Protection

Sensitive information is less centrally located as it is processed and stored locally on separate devices. When compared to conventional centralized learning techniques, this structure reduces the likelihood of significant breaches.

Effective Use of Data

Federated learning may improve model performance and accuracy by using data from several devices or institutions rather than centrally gathering data. This makes it feasible for the model to learn from a large dataset, something that conventional approaches would not be able to do.

Lower Data Transfer Expenses

Federated learning decreases data transmission costs and network stress by sharing just model changes rather than raw data. Applications with poor connection or settings where bandwidth costs are an issue would particularly benefit from this.

Quicker Education and Instantaneous Updates

Models may be updated almost instantly as data is created on local devices with to federated learning. Applications where current learning is essential, such as smart devices or tailored suggestions, benefit from this responsiveness.

Observance of Data Regulations

Because the data remains locally, federated learning is well-suited to comply with data privacy rules and regulations like the GDPR. For businesses managing user data in regulated sectors like healthcare or banking, this may reduce compliance concerns.

Increased Customization

Federated learning preserves user privacy while enabling models to be tailored to local data patterns. Applications such as customized advice or individualized health monitoring benefit greatly from this.

Conclusion

All things considered, federated learning facilitates safe, privacy-aware AI developments, enabling efficient data utilization without jeopardizing user confidence or legal compliance.

Read more on Govindhtech.com

[hitechnectar-htn]

Hello #TechGeeks, 

Did you know #FederatedLearning enables multiple devices to learn using a shared model? 

Did you know #FL is a #decentralized method in #MachineLearning? 

Click on the link below to learn more about Federated Learning and its applications across industries! 

https://bit.ly/3jEbkB6

#CyberSecurity #IoT #Healthcare #Finance #Fintech #distributedinfrastructures #ΑΙ #ArtificialIntelligence #DeepLearningAlgorithm #SentimentAnalysis #Python #NaturalLanguageProcessing #dataprivacy

[smestreet]

How Federated Learning is Going to Revolutionize AI

How #FederatedLearning is Going to Revolutionize #AI

Article By Ashwani Gupta, Senior Data Scientist, Publicis Sapient

This year, we observed an amazing astronomical phenomenon – a picture of a black hole – for the first time. But did you know, this black hole was more than 50 million light-years away? And to capture this picture, scientists required a single disk telescope as big as the size of the earth! Since it was practically impossible to…

[abantech-blog]

(vía https://www.youtube.com/watch?v=sCUFwEM5dFc)

Artificial intelligence on your smartphone: Federated learning on your mobile, not the cloud.

The company has begun to take advantage of the so-called "federated learning", a type of procedure that takes advantage of the ability of our smartphones to train the system and improve it without Google knowing virtually nothing...

#ArtificialIntelligence #AI #MachineLearning Abantech #Google #BigData #tech #IoT #ML #DeepLearning #fintech #technology #robotics #Federatedlearning #Google #smartphone