DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning

2026-05-13

AI Summary

This work addresses key limitations in secure aggregation for federated learning—namely, excessive communication rounds, high computational overhead from public-key operations, and poor robustness to client dropouts—by introducing a secret sharing–based distributed aggregator architecture. In this approach, a small committee of clients acts as aggregators: each participant secret-shares its local model update among committee members, who then compute partial aggregation results locally and return shares that enable the server to efficiently reconstruct the global model. By eliminating conventional local masking and homomorphic encryption, the proposed method substantially reduces both computation and communication costs. Experimental results demonstrate that, under a realistic setting with 100,000-dimensional update vectors and 100,000 5G clients, the protocol achieves a 4.6× speedup over the OPA protocol while maintaining strong privacy guarantees and system efficiency.

Abstract

Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server. Secure-aggregation schemes protect privacy against an honest-but-curious server, but existing approaches often suffer from many communication rounds, heavy public-key operations, or difficulty handling client dropouts. Recent methods like One-Shot Private Aggregation (OPA) cut rounds to a single server interaction per FL iteration, yet they impose substantial cryptographic and computational overhead on both server and clients. We propose a new protocol called DisAgg that leverages a small committee of clients called Aggregators to perform the aggregation itself: each client secret-shares its update vector to Aggregators, which locally compute partial sums and return only aggregated shares for server-side reconstruction. This design eliminates local masking and expensive homomorphic encryption, reducing endpoint computation while preserving privacy against a curious server and a limited fraction of colluding clients. By leveraging optimal trade-offs between communication and computation costs, DisAgg processes 100k-dimensional update vectors from 100k 5G clients with a 4.6x speedup compared to OPA, the previous best protocol.

Problem

• Federated Learning
• Secure Aggregation
• Privacy
• Client Dropouts
• Communication Efficiency

Innovation

• Secure Aggregation
• Federated Learning
• Secret Sharing
• Distributed Aggregators
• Communication-Efficient Privacy