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

arXiv: https://arxiv.org/abs/2606.14354 · HTML: https://mlsanity.com/paper/2606.14354/

## 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.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Sparsified SGD with Memory](https://mlsanity.com/paper/1809.07599/) (2018)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [GreedyGD: Enhanced Generalized Deduplication for Direct Analytics in IoT](https://mlsanity.com/paper/2304.07240/) (2023)
- [EntroGD: Scalable Generalized Deduplication for Efficient Direct Analytics on Compressed IoT Data](https://mlsanity.com/paper/2511.04148/) (2025)

## 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.