DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
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 agg...
| Authors | Haaris Mehmood, Giorgos Tatsis, Dimitrios Alexopoulos et al. |
|---|---|
| Primary category | cs.CR |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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