Federated Learning Unveiled: From Practical Insights to Bold Predictions DOI

M. Bharathi,

T. Aditya Sai Srinivas

Journal of IoT and Machine Learning., Journal Year: 2025, Volume and Issue: 2(1), P. 1 - 10

Published: Jan. 13, 2025

Federated Learning (FL) is a collaborative machine learning technique that lets multiple entities train shared model without exchanging their data, keeping privacy intact. Over the past decade, FL has evolved, scaling to millions of devices in various domains, all while maintaining strong differential (DP) protections. Companies like Google, Apple, and Meta have successfully brought life systems, proving its real-world value. However, challenges remain. Verifying server-side DP guarantees managing training across mix device types are complex issues need attention. Emerging trends large multi-modal models blurred lines between personalization pushing traditional limits. To address these challenges, we propose more flexible framework focused on principles, using trusted execution environments open-source collaboration drive future innovation.

Language: Английский

Federated Learning Unveiled: From Practical Insights to Bold Predictions DOI

M. Bharathi,

T. Aditya Sai Srinivas

Journal of IoT and Machine Learning., Journal Year: 2025, Volume and Issue: 2(1), P. 1 - 10

Published: Jan. 13, 2025

Federated Learning (FL) is a collaborative machine learning technique that lets multiple entities train shared model without exchanging their data, keeping privacy intact. Over the past decade, FL has evolved, scaling to millions of devices in various domains, all while maintaining strong differential (DP) protections. Companies like Google, Apple, and Meta have successfully brought life systems, proving its real-world value. However, challenges remain. Verifying server-side DP guarantees managing training across mix device types are complex issues need attention. Emerging trends large multi-modal models blurred lines between personalization pushing traditional limits. To address these challenges, we propose more flexible framework focused on principles, using trusted execution environments open-source collaboration drive future innovation.

Language: Английский

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