mlsanityData to 14 Oct 2026

Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers

arXiv 2511.22616 ↗First posted 27 Nov 2025cs.LG cs.DCcited by 3

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Abstract (arXiv)

The integration of IoT and AI has unlocked innovation across industries, but growing privacy concerns and data isolation hinder progress. Traditional centralized ML struggles to overcome these challenges, which has led to the rise of Federated Learning (FL), a decentralized paradigm that enables collaborative model training without sharing local raw data. FL ensures data privacy, reduces communication overhead, and supports scalability, yet its heterogeneity adds complexity compared to centralized approaches. This survey focuses on three main FL research directions: personalization, optimization, and robustness, offering a structured classification through a hybrid methodology that combines bibliometric analysis with systematic review to identify the most influential works. We examine challenges and techniques related to heterogeneity, efficiency, security, and privacy, and provide a comprehensive overview of aggregation strategies, including architectures, synchronization methods, and diverse federation objectives. To complement this, we discuss practical evaluation approaches and present experiments comparing aggregation methods under IID and non-IID data distributions. Finally, we outline promising research directions to advance FL, aiming to guide future innovation in this rapidly evolving field.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 60 references that are in the map. First posted on arXiv 27 Nov 2025.

Builds on

22 of 60 references.

References · 22

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  3. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  4. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  5. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
  6. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 84
  7. 2021STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learningcited by 43
  8. 2020Throughput-Optimal Topology Design for Cross-Silo Federated Learningcited by 39
  9. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  10. 2021Fast-adapting and Privacy-preserving Federated Recommender Systemcited by 30
  11. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  12. 2021Learned Gradient Compression for Distributed Deep Learningcited by 20
10 more
  1. 2021Differential Privacy Meets Federated Learning under Communication Constraintscited by 16
  2. 2021Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthinesscited by 15
  3. 2022ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferencescited by 15
  4. 2023Securing Distributed SGD against Gradient Leakage Threatscited by 14
  5. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  6. 2021Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validationcited by 10
  7. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
  8. 2022FLAME: Federated Learning Across Multi-device Environmentscited by 8
  9. 2021Federated Learning via Plurality Votecited by 6
  10. 2022Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6Gcited by 1
  2. 2026Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentialscited by 0
  3. 2026Client-Conditional Federated Learning via Local Training Data Statisticscited by 0