mlsanityData to 7 Oct 2026

Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation

arXiv 2210.01161 ↗First posted 3 Oct 2022cs.LG cs.AI cs.DCcited by 11

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Abstract (arXiv)

Synchronous updates may compromise the efficiency of cross-device federated learning once the number of active clients increases. The textitFedBuff algorithm (Nguyen et al., 2022) alleviates this problem by allowing asynchronous updates (staleness), which enhances the scalability of training while preserving privacy via secure aggregation. We revisit the textitFedBuff algorithm for asynchronous federated learning and extend the existing analysis by removing the boundedness assumptions from the gradient norm. This paper presents a theoretical analysis of the convergence rate of this algorithm when heterogeneity in data, batch size, and delay are considered.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 19 of its 24 references that are in the map. First posted on arXiv 3 Oct 2022.

Builds on

19 of 24 references.

References · 19

  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. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  5. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  6. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  7. 2021A Field Guide to Federated Optimizationcited by 312
  8. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  9. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
  10. 2019Asynchronous Federated Optimizationcited by 264
  11. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  12. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
7 more
  1. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 127
  2. 2019Decentralized Deep Learning with Arbitrary Communication Compressioncited by 109
  3. 2022Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learningcited by 76
  4. 2022Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delayscited by 61
  5. 2020Advances in Asynchronous Parallel and Distributed Optimizationcited by 40
  6. 2015An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimizationcited by 38
  7. 2019Asynchronous Federated Learning with Differential Privacy for Edge Intelligencecited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 2; 2026: 1.

11 papers.

  1. 2024FADAS: Towards Federated Adaptive Asynchronous Optimizationcited by 10
  2. 2024Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailabilitycited by 9
  3. 2024Quantized and Asynchronous Federated Learningcited by 3
  4. 2023Convergence Analysis of Decentralized ASGDcited by 2
  5. 2024Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delayscited by 2
  6. 2023FAVANO: Federated AVeraging with Asynchronous NOdescited by 1
  7. 2024Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Datacited by 1
  8. 2023Serverless Federated Learning with flwr-serverlesscited by 0
  9. 2025Analysis of Asynchronous Federated Learning: Unraveling the Interactions between Gradient Compression, Delay, and Data Heterogeneitycited by 0
  10. 2025Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learningcited by 0
  11. 2026Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networkscited by 0