mlsanityData to 7 Oct 2026

Secure Aggregation for Buffered Asynchronous Federated Learning

arXiv 2110.02177 ↗First posted 5 Oct 2021cs.LG cs.CR cs.DCcited by 10

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Abstract (arXiv)

Federated learning (FL) typically relies on synchronous training, which is slow due to stragglers. While asynchronous training handles stragglers efficiently, it does not ensure privacy due to the incompatibility with the secure aggregation protocols. A buffered asynchronous training protocol known as FedBuff has been proposed recently which bridges the gap between synchronous and asynchronous training to mitigate stragglers and to also ensure privacy simultaneously. FedBuff allows the users to send their updates asynchronously while ensuring privacy by storing the updates in a trusted execution environment (TEE) enabled private buffer. TEEs, however, have limited memory which limits the buffer size. Motivated by this limitation, we develop a buffered asynchronous secure aggregation (BASecAgg) protocol that does not rely on TEEs. The conventional secure aggregation protocols cannot be applied in the buffered asynchronous setting since the buffer may have local models corresponding to different rounds and hence the masks that the users use to protect their models may not cancel out. BASecAgg addresses this challenge by carefully designing the masks such that they cancel out even if they correspond to different rounds. Our convergence analysis and experiments show that BASecAgg almost has the same convergence guarantees as FedBuff without relying on TEEs.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 5 Oct 2021.

Builds on

17 of 31 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2020Adaptive Federated Optimizationcited by 1,036
  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. 2019Asynchronous Federated Optimizationcited by 264
  7. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  8. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  9. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  10. 2020LDP-Fed: Federated Learning with Local Differential Privacycited by 129
  11. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  12. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 60
5 more
  1. 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 38
  2. 2019Federated Learning with Autotuned Communication-Efficient Secure Aggregationcited by 23
  3. 2020Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noisecited by 21
  4. 2020Secure Aggregation with Heterogeneous Quantization in Federated Learningcited by 21
  5. 2021Information Theoretic Secure Aggregation with User Dropoutscited by 19

Built on by

Cited by year

Citing papers per year: 2021: 4; 2022: 1; 2023: 1; 2024: 4.

10 papers.

  1. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
  2. 2021Eluding Secure Aggregation in Federated Learning via Model Inconsistencycited by 66
  3. 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
  4. 2021CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learningcited by 20
  5. 2023FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Schedulercited by 13
  6. 2022Straggler-Resilient Personalized Federated Learningcited by 9
  7. 2024FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweightingcited by 5
  8. 2024Learning from straggler clients in federated learningcited by 1
  9. 2024Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizationscited by 0
  10. 2024Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learningcited by 0