mlsanityData to 7 Oct 2026

Towards Efficient Communications in Federated Learning: A Contemporary Survey

arXiv 2208.01200 ↗First posted 2 Aug 2022cs.DCcited by 24

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Abstract (arXiv)

In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a particular distributed machine learning procedure with privacy protection mechanisms, which can achieve multi-party collaborative computing without revealing the original data. However, in practice, FL faces a variety of challenging communication problems. This review seeks to elucidate the relationship between these communication issues by methodically assessing the development of FL communication research from three perspectives: communication efficiency, communication environment, and communication resource allocation. Firstly, we sort out the current challenges existing in the communications of FL. Second, we have collated FL communications-related papers and described the overall development trend of the field based on their logical relationship. Ultimately, we discuss the future directions of research for communications in FL.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 8; 2024: 8; 2025: 5; 2026: 2.

24 papers.

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  1. 2022Distributed Linear Bandits under Communication Constraintscited by 8
  2. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  3. 2023A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regretcited by 7
  4. 2023Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Drivingcited by 7
  5. 2024FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clusteringcited by 5
  6. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  7. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  8. 2024A survey on secure decentralized optimization and learningcited by 4
  9. 2023A review of federated learning in renewable energy applications: Potential, challenges, and future directionscited by 3
  10. 2024Enhancing Privacy in Federated Learning through Local Trainingcited by 3
  11. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  12. 2023Federated PAC-Bayesian Learning on Non-IID datacited by 2
12 more
  1. 2023Sparse Training for Federated Learning with Regularized Error Correctioncited by 2
  2. 2025Towards One-shot Federated Learning: Advances, Challenges, and Future Directionscited by 2
  3. 2023Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Promptcited by 1
  4. 2024SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environmentcited by 1
  5. 2024FedFT: Improving Communication Performance for Federated Learning with Frequency Space Transformationcited by 1
  6. 2025Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimizationcited by 1
  7. 2024Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learningcited by 0
  8. 2025Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learningcited by 0
  9. 2025Pinching Antennas Meet AI in Next-Generation Wireless Networkscited by 0
  10. 2025Communication-Efficient Learning for Satellite Constellationscited by 0
  11. 2026Beyond Weights and Gradients: A Taxonomy of Federated Learning Messagescited by 0
  12. 2026Quantization in Federated Learning: Methods, Challenges and Future Directionscited by 0