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Federated Learning: Privacy, Technology, and Governance in AI
Overview of Federated Learning (FL), detailing its fundamental shift from centralized data aggregation to a decentralized approach where models are trained locally on client data, sharing only updates.
It explains FL's technical architecture, including the Federated Averaging (FedAvg) algorithm and its advancements, and explores various privacy-enhancing technologies (PETs) like Secure Aggregation, Differential Privacy, and Homomorphic Encryption, while acknowledging their trade-offs.
The text further addresses systemic challenges such as data and system heterogeneity, communication overhead, and scalability, along with real-world applications in sectors like healthcare and finance.
Finally, it discusses governance, ethical implications, including legal compliance, fairness, and accountability, and projects the future trajectory of FL, emphasizing personalized models, efficiency, trustworthiness, and its broader impact on data governance and the data economy.
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By Benjamin Alloul 🗪 🅽🅾🆃🅴🅱🅾🅾🅺🅻🅼Overview of Federated Learning (FL), detailing its fundamental shift from centralized data aggregation to a decentralized approach where models are trained locally on client data, sharing only updates.
It explains FL's technical architecture, including the Federated Averaging (FedAvg) algorithm and its advancements, and explores various privacy-enhancing technologies (PETs) like Secure Aggregation, Differential Privacy, and Homomorphic Encryption, while acknowledging their trade-offs.
The text further addresses systemic challenges such as data and system heterogeneity, communication overhead, and scalability, along with real-world applications in sectors like healthcare and finance.
Finally, it discusses governance, ethical implications, including legal compliance, fairness, and accountability, and projects the future trajectory of FL, emphasizing personalized models, efficiency, trustworthiness, and its broader impact on data governance and the data economy.