Achieving Participative and Privacy-preserving Federated Learning
Year: 2025
Type: dissertation
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 achieve maximum social welfare. To resolve this inefficiency, the Multi-player Multi-action Zero-Determinant (MMZD) strategy and its cooperative extension, the MMZD Alliance (MMZDA), are developed to maximize social welfare. These strategies enable each organization, or a coalition of them, to unilaterally control and enhance the global welfare without incurring additional cost. Experimental results verify that MMZD effectively achieves the theoretical social optimum, and that MMZDA further improves the maximum attainable welfare. The second part focuses on privacy preservation in Hierarchical Federated Learning (HFL), where a client-edge-cloud structure accelerates training and reduces communication cost but increases the risk of privacy leakage. To address this issue, a novel differential privacy definition tailored for HFL, Group Local Differential Privacy (GLDP), is proposed, along with the Sampling-Randomizing-Shuffling (SRS) mechanism to implement it. SRS combines randomized response and shuffling with a sampling process that achieves stronger privacy protection while adding less noise. Comprehensive theoretical analyses provide guarantees for both privacy and convergence, and experiments on real-world datasets demonstrate that SRS effectively balances privacy protection and model performance in HFL. The final part explores federated unlearning (FUI) under Differentially Private Federated Learning (DPFL), enabling the removal of a clients data influence while maintaining privacy guarantees. Unlike existing federated unlearning methods that fail under DP noise, the proposed Federated Unlearning with Indistinguishability (FUI) framework performs local model retraction followed by global noise calibration, yielding an unlearned model that is statistically indistinguishable from the retrained counterpart. A two-stage Stackelberg game is formulated to derive optimal unlearning strategies for both the server and the target client. Privacy and convergence analyses provide formal guarantees, and experiments on four benchmark datasets show that FUI achieves superior accuracy and efficiency compared with existing unlearning approaches. Extensive empirical evaluations on datasets such as Adult, Purchase, MNIST, and CIFAR10 demonstrate that the proposed mechanisms-MMZDA for incentive alignment, GLDP with SRS for hierarchical privacy preservation, and FUI for accountable deletionjointly enhance the reliability and practicality of federated learning. Together, these contributions establish a cohesive framework that advances the development of participative, privacy-preserving, and trustworthy federated intelligence.
Source: Figshare
Author: Chen, Jianan
Institution: Purdue University System
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7114782219