FedPerm: Private and Robust Federated Learning by Parameter Permutation
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Abstract (arXiv)
Federated Learning (FL) is a distributed learning paradigm that enables mutually untrusting clients to collaboratively train a common machine learning model. Client data privacy is paramount in FL. At the same time, the model must be protected from poisoning attacks from adversarial clients. Existing solutions address these two problems in isolation. We present FedPerm, a new FL algorithm that addresses both these problems by combining a novel intra-model parameter shuffling technique that amplifies data privacy, with Private Information Retrieval (PIR) based techniques that permit cryptographic aggregation of clients' model updates. The combination of these techniques further helps the federation server constrain parameter updates from clients so as to curtail effects of model poisoning attacks by adversarial clients. We further present FedPerm's unique hyperparameters that can be used effectively to trade off computation overheads with model utility. Our empirical evaluation on the MNIST dataset demonstrates FedPerm's effectiveness over existing Differential Privacy (DP) enforcement solutions in FL.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 29 of its 59 references that are in the map. First posted on arXiv 16 Aug 2022.
Builds on
29 of 59 references.
References · 29
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
- 2019Deep Leakage from Gradientscited by 1,004
- 2018How To Backdoor Federated Learningcited by 745
- 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
- 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
- 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
- 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
- 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
- 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 285
- 2019Can You Really Backdoor Federated Learning?cited by 250
17 more
- 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
- 2018Amplification by Shuffling: From Local to Central Differential Privacy via Anonymitycited by 163
- 2020LDP-Fed: Federated Learning with Local Differential Privacycited by 129
- 2018Protection Against Reconstruction and Its Applications in Private Federated Learningcited by 127
- 2018Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learningcited by 75
- 2019The Privacy Blanket of the Shuffle Modelcited by 73
- 2020Local and Central Differential Privacy for Robustness and Privacy in Federated Learningcited by 72
- 2022Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakagecited by 72
- 2020A Framework for Evaluating Gradient Leakage Attacks in Federated Learningcited by 63
- 2019HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learningcited by 51
- 2019Collecting and Analyzing Multidimensional Data with Local Differential Privacycited by 46
- 2020FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selectioncited by 35
- 2020FLAME: Differentially Private Federated Learning in the Shuffle Modelcited by 27
- 2019Scalable and Differentially Private Distributed Aggregation in the Shuffled Modelcited by 20
- 2019Separating Local & Shuffled Differential Privacy via Histogramscited by 16
- 2021FLAME: Taming Backdoors in Federated Learning (Extended Version 1)cited by 16
- 2019Differentially Private Summation with Multi-Message Shufflingcited by 9
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