Federated Learning (FL) is a promising framework for multiple clients to learn a joint model without directly sharing the data. In addition to high utility of the joint model, rigorous privacy protection of the data and communication efficiency are important design goals. Many existing efforts achieve rigorous privacy by ensuring differential privacy for intermediate model parameters, however, they assume a uniform privacy parameter for all the clients. In practice, different clients may have different privacy requirements due to varying policies or preferences. In this paper, we focus on explicitly modeling and leveraging the heterogeneous privacy requirements of different clients and study how to optimize utility for the joint model while minimizing communication cost. As differentially private perturbations affect the model utility, a natural idea is to make better use of information submitted by the clients with higher privacy budgets (referred to as "public" clients, and the opposite as "private" clients). The challenge is how to use such information without biasing the joint model. We propose P rojected F ederated A veraging (PFA), which extracts the top singular subspace of the model updates submitted by "public" clients and utilizes them to project the model updates of "private" clients before aggregating them. We then propose communication-efficient PFA+, which allows "private" clients to upload projected model updates instead of original ones. Our experiments verify the utility boost of both algorithms compared to the baseline methods, whereby PFA+ achieves over 99% uplink communication reduction for "private" clients.
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Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
Providing privacy protection has been one of the primary motivations of Federated Learning
(FL). Recently, there has been a line of work on incorporating the formal privacy notion of
differential privacy with FL. To guarantee the client-level differential privacy in FL algorithms,
the clients’ transmitted model updates have to be clipped before adding privacy noise. Such clipping operation is substantially different from its counterpart of gradient clipping in the
centralized differentially private SGD and has not been well-understood. In this paper, we
first empirically demonstrate that the clipped FedAvg can perform surprisingly well even
with substantial data heterogeneity when training neural networks, which is partly because the clients’ updates become similar for several popular deep architectures. Based on this key
observation, we provide the convergence analysis of a differential private (DP) FedAvg algorithm and highlight the relationship between clipping bias and the distribution of the clients’ updates. To the best of our knowledge, this is the first work that rigorously investigates theoretical and empirical issues regarding the clipping operation
in FL algorithms.
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- NSF-PAR ID:
- 10341717
- Date Published:
- Journal Name:
- International Conference on Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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