Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation

Mohamed Seif Eldin Mohamed, Wei Ting Chang, Ravi Tandon

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a fading multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as O(1/K1/2) , where K is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by jointly incorporating both wireless aggregation and user sampling, one can obtain even stronger privacy guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as O(1/K3/4). In addition, we show that LDP is also boosted by user sampling. We also present analysis for the convergence rate of the proposed scheme and study the tradeoffs between wireless resources, convergence, and privacy theoretically and empirically for two scenarios when the number of sampled participants are (a) known, or (b) unknown at the parameter server.

Original languageEnglish (US)
Pages (from-to)3821-3835
Number of pages15
JournalIEEE Journal on Selected Areas in Communications
Issue number12
StatePublished - Dec 1 2021


  • Federated learning
  • differential privacy
  • user sampling
  • wireless aggregation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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