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MUFFLe paper proposes efficient model update compression for federated learning

By PulseAugur Editorial · [2 sources] ·

A new paper introduces MUFFLe, a method designed to reduce the communication costs associated with federated learning. MUFFLe achieves this by integrating generalized deduplication into the FedAvg pipeline, effectively compressing model updates by identifying and removing repeated patterns. Preliminary tests on the MNIST dataset indicate that MUFFLe significantly lowers uplink communication requirements compared to other compression techniques like 8-bit quantization and Top-k sparsification, while still reaching a target accuracy. AI

IMPACT This research could lead to more efficient federated learning deployments by reducing communication overhead, enabling wider adoption in resource-constrained environments.

RANK_REASON The cluster contains an academic paper detailing a new method for federated learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

MUFFLe paper proposes efficient model update compression for federated learning

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaobo Zhao, Daniel E. Lucani ·

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

    arXiv:2606.14354v1 Announce Type: new Abstract: 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 inte…

  2. arXiv cs.LG TIER_1 English(EN) · Daniel E. Lucani ·

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

    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 F…

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