mlsanityData to 7 Oct 2026

Advancements in Federated Learning: Models, Methods, and Privacy

arXiv 2302.11466 ↗First posted 22 Feb 2023cs.AI cs.CLcited by 7

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Abstract (arXiv)

Federated learning (FL) is a promising technique for addressing the rising privacy and security issues. Its main ingredient is to cooperatively learn the model among the distributed clients without uploading any sensitive data. In this paper, we conducted a thorough review of the related works, following the development context and deeply mining the key technologies behind FL from both theoretical and practical perspectives. Specifically, we first classify the existing works in FL architecture based on the network topology of FL systems with detailed analysis and summarization. Next, we abstract the current application problems, summarize the general techniques and frame the application problems into the general paradigm of FL base models. Moreover, we provide our proposed solutions for model training via FL. We have summarized and analyzed the existing FedOpt algorithms, and deeply revealed the algorithmic development principles of many first-order algorithms in depth, proposing a more generalized algorithm design framework. Based on these frameworks, we have instantiated FedOpt algorithms. As privacy and security is the fundamental requirement in FL, we provide the existing attack scenarios and the defense methods. To the best of our knowledge, we are among the first tier to review the theoretical methodology and propose our strategies since there are very few works surveying the theoretical approaches. Our survey targets motivating the development of high-performance, privacy-preserving, and secure methods to integrate FL into real-world applications.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 105 of its 209 references that are in the map. First posted on arXiv 22 Feb 2023.

Builds on

105 of 209 references.

References · 105

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38 more
  1. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  2. 2017Federated Multi-Task Learningcited by 669
  3. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
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  9. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
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  12. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
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  26. 2018Blockchained On-Device Federated Learningcited by 105
  27. 2019Secure Federated Matrix Factorizationcited by 102
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  29. 2015Multi-task Sequence to Sequence Learningcited by 94
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  33. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
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50 most cited of 105 All 105 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 2; 2026: 1.

7 papers.

  1. 2024Transforming Wearable Data into Personal Health Insights using Large Language Model Agentscited by 19
  2. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  3. 2023An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneitycited by 5
  4. 2024Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregationcited by 1
  5. 2026Training Memory in Deep Neural Networks: Mechanisms, Evidence, and Measurement Gapscited by 1
  6. 2025Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Surveycited by 0
  7. 2025Agentic Meta-Orchestrator for Multi-task Copilotscited by 0