mlsanityData to 7 Oct 2026

GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning

arXiv 2212.01523 ↗First posted 3 Dec 2022cs.LG cs.DCcited by 7

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Abstract (arXiv)

Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy. However, the substantial communication overhead of FL makes training challenging when edge devices have limited network bandwidth. Existing work to optimize FL bandwidth overlooks downstream transmission and does not account for FL client sampling. In this paper we propose GlueFL, a framework that incorporates new client sampling and model compression algorithms to mitigate low download bandwidths of FL clients. GlueFL prioritizes recently used clients and bounds the number of changed positions in compression masks in each round. Across three popular FL datasets and three state-of-the-art strategies, GlueFL reduces downstream client bandwidth by 27% on average and reduces training time by 29% on average.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 34 of its 50 references that are in the map. First posted on arXiv 3 Dec 2022.

Builds on

34 of 50 references.

References · 34

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22 more
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Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 3.

7 papers.

  1. 2024FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extractioncited by 21
  2. 2024Towards Federated RLHF with Aggregated Client Preference for LLMscited by 11
  3. 2025CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimizationcited by 7
  4. 2023Enhancing Neural Training via a Correlated Dynamics Modelcited by 5
  5. 2025FedFetch: Faster Federated Learning with Adaptive Downstream Prefetchingcited by 2
  6. 2023Venn: Resource Management for Collaborative Learning Jobscited by 1
  7. 2025Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Maskingcited by 0