DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning

• URL: http://arxiv.org/abs/2605.13708v1
• Date: Wed, 13 May 2026 15:56:12 GMT
• Title: DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
• Abstract summary: Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server.<n>We propose a new protocol called DisAgg that leverages a small committee of clients called Aggregators to perform the aggregation itself.<n>DisAgg processes 100k-dimensional update vectors from 100k 5G clients with a 4.6x speedup compared to OPA, the previous best protocol.
• Score: 23.144047085164825
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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