mlsanityData to 14 Oct 2026

MUFFLe: Efficient Model Update Compression via Generalized Deduplication for Federated Learning

arXiv 2606.14354 ↗First posted 12 Jun 2026cs.LGcited by 0

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Abstract (arXiv)

Federated learning is well suited to edge environments but is often limited by the uplink cost of transmitting model updates. This Work-in-Progress paper presents MUFFLe, a communication-efficient update compression scheme that integrates generalized deduplication (GD) into the FedAvg pipeline. MUFFLe deduplicates repeated patterns across the update vector, yielding a fixed-rate, variable-count compression scheme. Preliminary experiments on IID MNIST with 20 clients show that MUFFLe reaches the target accuracy of 92.93% with 38~MB cumulative uplink communication, compared with 75~MB for 8-bit quantization, 86~MB for Top-k sparsification, and 310~MB for uncompressed FedAvg. These results demonstrate the feasibility of applying GD to communication-efficient federated learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 12 Jun 2026.

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6 of 11 references.

References · 6

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  3. 2018Sparsified SGD with Memorycited by 376
  4. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  5. 2023GreedyGD: Enhanced Generalized Deduplication for Direct Analytics in IoTcited by 3
  6. 2025EntroGD: Scalable Generalized Deduplication for Efficient Direct Analytics on Compressed IoT Datacited by 1

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  1. 2023FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications
  2. 2021FEDZIP: A Compression Framework for Communication-Efficient Federated Learning
  3. 2024Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
  4. 2026A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments
  5. 2025FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust & Efficient Federated Learning
  6. 2026Adaptive Heterogeneous Compression for Resource-Efficient Federated Knowledge Distillation
  7. 2022QUIC-FL: Quick Unbiased Compression for Federated Learning
  8. 2021Communication-Efficient Federated Learning via Predictive Coding