mlsanityData to 7 Oct 2026

Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation

arXiv 2308.00263 ↗First posted 1 Aug 2023cs.LG eess.SP math.OCcited by 10

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Abstract (arXiv)

Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability. However, it has a high communication cost, which has not been examined with quantized communications. To tackle this problem, we present a new algorithm (QAFeL), with a quantization scheme that establishes a shared "hidden" state between the server and clients to avoid the error propagation caused by direct quantization. This approach allows for high precision while significantly reducing the data transmitted during client-server interactions. We provide theoretical convergence guarantees for QAFeL and corroborate our analysis with experiments on a standard benchmark.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 31 of its 44 references that are in the map. First posted on arXiv 1 Aug 2023.

Builds on

31 of 44 references.

References · 31

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19 more
  1. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 306
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Built on by

Cited by year (2026 to 7 Oct)

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

10 papers.

  1. 2024FADAS: Towards Federated Adaptive Asynchronous Optimizationcited by 10
  2. 2024pfl-research: simulation framework for accelerating research in Private Federated Learningcited by 8
  3. 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
  4. 2024Quantized and Asynchronous Federated Learningcited by 3
  5. 2024Communication Compression for Distributed Learning without Control Variatescited by 2
  6. 2025PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learningcited by 1
  7. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  8. 2025Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learningcited by 0
  9. 2025Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedbackcited by 0
  10. 2026Decentralized Parameter-Free Online Learning with Compressed Gossipcited by 0