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Motivated by the ever-increasing concerns on personal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learning setting. An FL system is comprised of a central parameter server and multiple local clients. It keeps data at local clients and learns a centralized model by sharing the model parameters learned locally. No local data needs to be shared, and privacy can be well protected. Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients. Furthermore, it is challenging to identify malicious clients since no local client data is available on the server. Besides, membership inference attacks can still be performed by using the uploaded model to estimate the client's local data, leading to privacy disclosure. In this work, we first propose a model update based federated averaging algorithm to defend against Byzantine attacks such as additive noise attacks and sign-flipping attacks. The individual client model initialization method is presented to provide further privacy protections from the membership inference attacks by hiding the individual local machine learning model. When combining these two schemes, privacy and security can be both effectively enhanced. The proposed schemes are proved to converge experimentally under non-lID data distribution when there are no attacks. Under Byzantine attacks, the proposed schemes perform much better than the classical model based FedAvg algorithm.
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This content will become publicly available on November 2, 2025
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning
Model pruning has been proposed as a technique for reducing the size and complexity of Federated learning (FL) models. By making local models coarser, pruning is intuitively expected to improve protection against privacy attacks. However, the level of this expected privacy protection has not been previously characterized, or optimized jointly with utility. In this paper, we first characterize the privacy offered by pruning. We establish information-theoretic upper bounds on the information leakage from pruned FL and we experimentally validate them under state-of-the-art privacy attacks across different FL pruning schemes. Second, we introducePriPrune– a privacy-aware algorithm for pruning in FL.PriPruneuses defense pruning masks, which can be applied locally afteranypruning algorithm, and adapts the defense pruning rate to jointly optimize privacy and accuracy. Another key idea in the design ofPriPruneisPseudo-Pruning: it undergoes defense pruning within the local model and only sends the pruned model to the server; while the weights pruned out by defense mask are withheld locally for future local training rather than being removed. We show thatPriPrunesignificantly improves the privacy-accuracy tradeoff compared to state-of-the-art pruned FL schemes. For example, on the FEMNIST dataset,PriPruneimproves the privacy ofPruneFLby 45.5% without reducing accuracy.
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- PAR ID:
- 10585580
- Publisher / Repository:
- ACM Digital Library
- Date Published:
- Journal Name:
- ACM Transactions on Modeling and Performance Evaluation of Computing Systems
- ISSN:
- 2376-3639
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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