Faithful Edge Federated Learning: Scalability and Privacy
This addresses the overlooked issue of agent faithfulness in federated learning, which is crucial for scalable and private deployment in decentralized edge networks, representing a novel formulation rather than an incremental improvement.
The paper tackles the problem of ensuring agents faithfully execute federated learning algorithms by analyzing how unbalanced and non-i.i.d. data affects incentives, revealing that agents with atypical data or more samples are prone to opt out or tamper. It designs two mechanisms—Faithful Federated Learning (FFL) and a scalable, differentially private version (DP-FFL)—that achieve economic properties like optimality and voluntary participation, with payment computation time complexity of O(1) in the number of agents.
Federated learning enables machine learning algorithms to be trained over a network of multiple decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that agents (e.g., mobile devices) faithfully execute the intended algorithm, which has been largely overlooked in the literature. In this study, we first use risk bounds to analyze how the key feature of federated learning, unbalanced and non-i.i.d. data, affects agents' incentives to voluntarily participate and obediently follow traditional federated learning algorithms. To be more specific, our analysis reveals that agents with less typical data distributions and relatively more samples are more likely to opt out of or tamper with federated learning algorithms. To this end, we formulate the first faithful implementation problem of federated learning and design two faithful federated learning mechanisms which satisfy economic properties, scalability, and privacy. Further, the time complexity of computing all agents' payments in the number of agents is $\mathcal{O}(1)$. First, we design a Faithful Federated Learning (FFL) mechanism which approximates the Vickrey-Clarke-Groves (VCG) payments via an incremental computation. We show that it achieves (probably approximate) optimality, faithful implementation, voluntary participation, and some other economic properties (such as budget balance). Second, by partitioning agents into several subsets, we present a scalable VCG mechanism approximation. We further design a scalable and Differentially Private FFL (DP-FFL) mechanism, the first differentially private faithful mechanism, that maintains the economic properties. Our mechanism enables one to make three-way performance tradeoffs among privacy, the iterations needed, and payment accuracy loss.