mlsanityData to 14 Oct 2026

QUIC-FL: Quick Unbiased Compression for Federated Learning

arXiv 2205.13341 ↗First posted 26 May 2022cs.LG cs.AI cs.DScited by 7

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Abstract (arXiv)

Distributed Mean Estimation (DME), in which n clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-efficient federated learning. In this paper, we improve on previous DME techniques that achieve the optimal O(1/n) Normalized Mean Squared Error (NMSE) guarantee by asymptotically improving the complexity for either encoding or decoding (or both). To achieve this, we formalize the problem in a novel way that allows us to use off-the-shelf mathematical solvers to design the quantization.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 54 of its 112 references that are in the map. First posted on arXiv 26 May 2022.

Builds on

54 of 112 references.

References · 54

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38 more
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50 most cited of 54 All 54 (JSON)

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Cited by year

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

7 papers.

  1. 2022Sparse Random Networks for Communication-Efficient Federated Learningcited by 32
  2. 2023THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compressioncited by 15
  3. 2022ScionFL: Efficient and Robust Secure Quantized Aggregationcited by 8
  4. 2023Adaptive Compression in Federated Learning via Side Informationcited by 6
  5. 2025PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Modelscited by 3
  6. 2024Optimal and Near-Optimal Adaptive Vector Quantizationcited by 2
  7. 2025Trading-off Accuracy and Communication Cost in Federated Learningcited by 0

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  1. 2023FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications
  2. 2024Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
  3. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
  4. 2021Communication-Efficient Federated Learning via Predictive Coding
  5. 2024FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
  6. 2025FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust & Efficient Federated Learning
  7. 2023Compression with Exact Error Distribution for Federated Learning
  8. 2024Rate-Constrained Quantization for Communication-Efficient Federated Learning