# Towards Efficient Communications in Federated Learning: A Contemporary Survey

arXiv: https://arxiv.org/abs/2208.01200 · HTML: https://mlsanity.com/paper/2208.01200/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2022.

## Built on by (most cited first, 24 of 24)

- [Distributed Linear Bandits under Communication Constraints](https://mlsanity.com/paper/2211.02212/) (2022)
- [Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks](https://mlsanity.com/paper/2304.08996/) (2023)
- [A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret](https://mlsanity.com/paper/2301.08869/) (2023)
- [Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2306.16169/) (2023)
- [FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering](https://mlsanity.com/paper/2404.15384/) (2024)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective](https://mlsanity.com/paper/2405.20431/) (2024)
- [A survey on secure decentralized optimization and learning](https://mlsanity.com/paper/2408.08628/) (2024)
- [A review of federated learning in renewable energy applications: Potential, challenges, and future directions](https://mlsanity.com/paper/2312.11220/) (2023)
- [Enhancing Privacy in Federated Learning through Local Training](https://mlsanity.com/paper/2403.17572/) (2024)
- [Review of Mathematical Optimization in Federated Learning](https://mlsanity.com/paper/2412.01630/) (2024)
- [Federated PAC-Bayesian Learning on Non-IID data](https://mlsanity.com/paper/2309.06683/) (2023)
- [Sparse Training for Federated Learning with Regularized Error Correction](https://mlsanity.com/paper/2312.13795/) (2023)
- [Towards One-shot Federated Learning: Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2505.02426/) (2025)
- [Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt](https://mlsanity.com/paper/2310.04455/) (2023)
- [SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment](https://mlsanity.com/paper/2407.18387/) (2024)
- [FedFT: Improving Communication Performance for Federated Learning with Frequency Space Transformation](https://mlsanity.com/paper/2409.05242/) (2024)
- [Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization](https://mlsanity.com/paper/2501.03222/) (2025)
- [Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning](https://mlsanity.com/paper/2411.10255/) (2024)
- [Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning](https://mlsanity.com/paper/2503.21627/) (2025)
- [Pinching Antennas Meet AI in Next-Generation Wireless Networks](https://mlsanity.com/paper/2511.07442/) (2025)
- [Communication-Efficient Learning for Satellite Constellations](https://mlsanity.com/paper/2511.20220/) (2025)
- [Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages](https://mlsanity.com/paper/2606.16891/) (2026)
- [Quantization in Federated Learning: Methods, Challenges and Future Directions](https://mlsanity.com/paper/2606.26822/) (2026)

## 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.