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Communication Efficiency in Federated Learning: Achievements and Challenges

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arxiv 2107.10996 v1 pith:EOMAIFU2 submitted 2021-07-23 cs.LG

Osama Shahid , Seyedamin Pouriyeh , Reza M. Parizi , Quan Z. Sheng , Gautam Srivastava , Liang Zhao This is my paper · ORCID
classification cs.LG
keywords communicationlearningchallengesdistributedemergingfederatedmachinetasks
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Federated Learning (FL) is known to perform Machine Learning tasks in a distributed manner. Over the years, this has become an emerging technology especially with various data protection and privacy policies being imposed FL allows performing machine learning tasks whilst adhering to these challenges. As with the emerging of any new technology, there are going to be challenges and benefits. A challenge that exists in FL is the communication costs, as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates this can create a communication bottleneck. In this paper, we present a survey of the research that is performed to overcome the communication constraints in an FL setting.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Robust Federated Learning Framework for Undependable Devices at Scale

    cs.LG 2024-12 conditional novelty 6.0 of 10

    FLUDE combines dependability-aware device selection, local model caching, and stale-aware model distribution to make federated learning faster, more accurate, and more efficient when many devices are unreliable.

  2. Enhancing Model Privacy in Federated Learning with Random Masking and Quantization

    cs.LG 2025-08 reject novelty 5.0 of 10

    FedQSN hides part of the server model with random masks and quantizes the remainder to give clients a degraded proxy, reporting a large global-vs-proxy performance gap with modest loss in the final global model.

  3. Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models

    cs.LG 2025-01 conditional novelty 4.0 of 10

    DP-FPL applies local DP to low-rank prompt factors and global DP to the shared prompt, reporting stronger accuracy under privacy than baselines.

  4. Adaptive Client Selection with Personalization for Communication Efficient Federated Learning

    cs.LG 2024-11 conditional novelty 4.0 of 10

    ACSP-FL combines below-average client selection, a decay schedule, and layer sharing with per-client personalization to reduce federated learning communication cost on HAR tasks.

  5. Encoded Spatial Attribute in Multi-Tier Federated Learning

    cs.LG 2025-01 reject novelty 2.0 of 10

    An under-specified N-tier federated learning design with spatial encoding reports 75.62% and 89.52% accuracy on two geospatial datasets, but the encoding mechanism is never defined.

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