DP-REC: Private & Communication-Efficient Federated Learning

• URL: http://arxiv.org/abs/2111.05454v1
• Date: Tue, 9 Nov 2021 23:33:11 GMT
• Title: DP-REC: Private & Communication-Efficient Federated Learning
• Authors: Aleksei Triastcyn, Matthias Reisser, Christos Louizos
• Abstract summary: We introduce a compression technique based on Relative Entropy Coding (REC) to the federated setting. With a minor modification to REC, we obtain a provably differentially private learning algorithm, DP-REC, and show how to compute its privacy guarantees. Our experiments demonstrate that DP-REC drastically reduces communication costs while providing privacy guarantees comparable to the state-of-the-art.
• Score: 16.884416092951007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Privacy and communication efficiency are important challenges in federated training of neural networks, and combining them is still an open problem. In this work, we develop a method that unifies highly compressed communication and differential privacy (DP). We introduce a compression technique based on Relative Entropy Coding (REC) to the federated setting. With a minor modification to REC, we obtain a provably differentially private learning algorithm, DP-REC, and show how to compute its privacy guarantees. Our experiments demonstrate that DP-REC drastically reduces communication costs while providing privacy guarantees comparable to the state-of-the-art.

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