| Paper ID | D1-S5-T4.2 |
| Paper Title |
Differentially Private Federated Learning with Shuffling and Client Self-Sampling |
| Authors |
Antonious Girgis, Deepesh Data, Suhas Diggavi, UCLA, United States |
| Session |
D1-S5-T4: Differential Privacy I |
| Chaired Session: |
Monday, 12 July, 23:20 - 23:40 |
| Engagement Session: |
Monday, 12 July, 23:40 - 00:00 |
| Abstract |
This paper studies a distributed optimization problem in the federated learning (FL) framework under differential privacy constraints, whereby a set of clients having local samples are connected to an untrusted server, who wants to learn a global model while preserving the privacy of clients' local datasets. We propose a new client sampling called \textit{self-sampling} that reflects the random availability of clients in the learning process in FL. We analyze the differential privacy of the SGD with client self-sampling by composing amplification by sampling along with amplification by shuffling. Furthermore, we analyze the convergence of the proposed SGD algorithm showing that we can get a reasonable learning performance while preserving the privacy of clients' data even with client self-sampling.
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