mlsanityData to 7 Oct 2026

SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning

arXiv 2203.13060 ↗First posted 24 Mar 2022cs.IT cs.DC cs.LGcited by 18

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Abstract (arXiv)

We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of N ∈ mathbbN distributed users, each of size L ∈ mathbbN, trained on their local data, in a privacy-preserving manner. SwiftAgg+ can significantly reduce the communication overheads without any compromise on security, and achieve optimal communication loads within diminishing gaps. Specifically, in presence of at most D=o(N) dropout users, SwiftAgg+ achieves a per-user communication load of (1+O(frac1N))L symbols and a server communication load of (1+O(frac1N))L symbols, with a worst-case information-theoretic security guarantee, against any subset of up to T=o(N) semi-honest users who may also collude with the curious server. Moreover, the proposed SwiftAgg+ allows for a flexible trade-off between communication loads and the number of active communication links. In particular, for T<N-D and for any K∈mathbbN, SwiftAgg+ can achieve the server communication load of (1+fracTK)L symbols, and per-user communication load of up to (1+fracT+DK)L symbols, where the number of pair-wise active connections in the network is fracN2(K+T+D+1).

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 24 Mar 2022.

Builds on

14 of 21 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  6. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  7. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  8. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  9. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  10. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 60
  11. 2021CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learningcited by 20
  12. 2021Information Theoretic Secure Aggregation with User Dropoutscited by 19
2 more
  1. 2020Communication-Computation Efficient Secure Aggregation for Federated Learningcited by 18
  2. 2022SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guaranteescited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 6; 2024: 1; 2025: 6; 2026: 2.

18 papers.

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  1. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  2. 2022DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharingcited by 17
  3. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  4. 2025Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Associationcited by 7
  5. 2023Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusioncited by 5
  6. 2023ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitmentcited by 4
  7. 2024Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimizationcited by 4
  8. 2023Survey of Federated Learning Models for Spatial-Temporal Mobility Applicationscited by 2
  9. 2025Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resiliencecited by 2
  10. 2022Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computationcited by 0
  11. 2022Secure Embedding Aggregation for Federated Representation Learningcited by 0
  12. 2023A Blockchain Solution for Collaborative Machine Learning over IoTcited by 0
6 more
  1. 2025Federated One-Shot Learning with Data Privacy and Objective-Hidingcited by 0
  2. 2025Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learningcited by 0
  3. 2025EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learningcited by 0
  4. 2025Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learningcited by 0
  5. 2026The Capacity of Information-Theoretic Secure Aggregation in Federated Learningcited by 0
  6. 2026Secure Aggregation with Top-K Sparsification in Decentralized Federated Learningcited by 0