Achieving participative and privacy-preserving federated learning
Abstract
The proliferation of data-driven intelligence has reshaped the foundations of artificial intelligence and machine learning, enabling unprecedented collaboration across distributed entities. Yet, as organizations increasingly rely on shared computation and collective learning, concerns over privacy, fairness, and accountability have become central. Federated Learning (FL) has emerged as a promising paradigm that allows multiple institutions to collaboratively train models without disclosing raw data. However, despite its decentralized structure, FL is not inherently trustworthy. Rational participants may contribute unevenly or strategically, privacy guarantees often degrade model utility, and once data contributions are incorporated, their removal becomes nearly impossible. These challenges reveal that building trustworthy federated intelligence requires more than algorithmic optimizationit demands principled mechanisms that ensure participation, protect privacy, and enable verifiable unlearning. This dissertation addresses these intertwined challenges through a unified theoretical and algorithmic framework for participative, privacy-preserving, and accountable federated learning. The first part investigates participation incentives in cross-silo FL, where multiple organizations jointly train a global model. The interactions among organizations are modeled as a public goods game, and theoretical analysis proves that a social dilemma arises in which the Nash equilibrium fails to…
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