mlsanityData to 14 Oct 2026

DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning

arXiv 2605.13708 ↗First posted 13 May 2026cs.CR cs.DC cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 13 May 2026.

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21 of 39 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2020Adaptive Federated Optimizationcited by 1,046
  7. 2019Deep Leakage from Gradientscited by 1,021
  8. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  9. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  10. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  11. 2021A Field Guide to Federated Optimizationcited by 315
  12. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 278
9 more
  1. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 246
  2. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 128
  3. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  4. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  5. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 64
  6. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 61
  7. 2023Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learningcited by 17
  8. 2025Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Associationcited by 7
  9. 2023OLYMPIA: A Simulation Framework for Evaluating the Concrete Scalability of Secure Aggregation Protocolscited by 2

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