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Reconciling Security and Communication Efficiency in Federated Learning
Karthik Prasad · Sayan Ghosh · Graham Cormode · Ilya Mironov · Ashkan Yousefpour · Pierre STOCK
Event URL: https://openreview.net/forum?id=ugPjyiiwlMR »

Cross-device Federated Learning is an increasingly popular machine learning setting to train a model by leveraging a large population of client devices with high privacy and security guarantees. However, communication efficiency remains a major bottleneck when scaling federated learning to production environments, particularly due to bandwidth constraints during uplink communication. In this paper, we formalize and address the problem of compressing client-to-server model updates under the Secure Aggregation primitive, a core component of Federated Learning pipelines that allows the server to aggregate the client updates without accessing them individually. In particular, we adapt standard scalar quantization and pruning methods to Secure Aggregation and propose Secure Indexing, a variant of Secure Aggregation that supports quantization for extreme compression. We establish state-of-the-art results on LEAF benchmarks in a secure Federated Learning setup with up to 40x compression in uplink communication and no meaningful loss in utility compared to uncompressed baselines.

Author Information

Karthik Prasad (Facebook AI)
Sayan Ghosh (University of Southern California)
Graham Cormode (Meta AI)
Ilya Mironov (Facebook / Meta)
Ashkan Yousefpour (Meta AI)
Pierre STOCK (Meta AI)

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