Secure Aggregation for Buffered Asynchronous Federated Learning

Abstract

Federated learning (FL) typically relies on synchronous training, which is slow due to stragglers. While asynchronous training handles stragglers efficiently, it does not ensure privacy due to the incompatibility with the secure aggregation protocols. A buffered asynchronous training protocol known as FedBuff has been proposed recently which bridges the gap between synchronous and asynchronous training to mitigate stragglers and to also ensure privacy simultaneously. FedBuff allows the users to send their updates asynchronously while ensuring privacy by storing the updates in a trusted execution environment (TEE) enabled private buffer. TEEs, however, have limited memory which limits the buffer size. Motivated by this limitation, we develop a buffered asynchronous secure aggregation (BASecAgg) protocol that does not rely on TEEs. The conventional secure aggregation protocols cannot be applied in the buffered asynchronous setting since the buffer may have local models corresponding to different rounds and hence the masks that the users use to protect their models may not cancel out. BASecAgg addresses this challenge by carefully designing the masks such that they cancel out even if they correspond to different rounds. Our convergence analysis and experiments show that BASecAgg almost has the same convergence guarantees as FedBuff without relying on TEEs.

arXiv admin note: substantial overlap with arXiv:2109.14236

References in corpus (6)

• Practical Secure Aggregation for Federated Learning on User-Held Data
• FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning
• Federated Learning with Buffered Asynchronous Aggregation
• Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
• Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise
• Secure Aggregation with Heterogeneous Quantization in Federated Learning

Cited by in corpus (1)

• Papaya: Practical, Private, and Scalable Federated Learning