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

arXiv: https://arxiv.org/abs/2511.22616 · HTML: https://mlsanity.com/paper/2511.22616/

## 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 (most cited first, 22 of 22)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning](https://mlsanity.com/paper/2106.10435/) (2021)
- [Throughput-Optimal Topology Design for Cross-Silo Federated Learning](https://mlsanity.com/paper/2010.12229/) (2020)
- [Non-IID data and Continual Learning processes in Federated Learning: A long road ahead](https://mlsanity.com/paper/2111.13394/) (2021)
- [Fast-adapting and Privacy-preserving Federated Recommender System](https://mlsanity.com/paper/2104.00919/) (2021)
- [Personalized Federated Learning with Adaptive Batchnorm for Healthcare](https://mlsanity.com/paper/2112.00734/) (2021)
- [Learned Gradient Compression for Distributed Deep Learning](https://mlsanity.com/paper/2103.08870/) (2021)
- [Differential Privacy Meets Federated Learning under Communication Constraints](https://mlsanity.com/paper/2101.12240/) (2021)
- [Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness](https://mlsanity.com/paper/2108.03980/) (2021)
- [ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences](https://mlsanity.com/paper/2207.13897/) (2022)
- [Securing Distributed SGD against Gradient Leakage Threats](https://mlsanity.com/paper/2305.06473/) (2023)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation](https://mlsanity.com/paper/2110.14285/) (2021)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [FLAME: Federated Learning Across Multi-device Environments](https://mlsanity.com/paper/2202.08922/) (2022)
- [Federated Learning via Plurality Vote](https://mlsanity.com/paper/2110.02998/) (2021)
- [Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregation](https://mlsanity.com/paper/2206.03623/) (2022)

## Built on by (most cited first, 3 of 3)

- [Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G](https://mlsanity.com/paper/2603.09141/) (2026)
- [Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials](https://mlsanity.com/paper/2602.02629/) (2026)
- [Client-Conditional Federated Learning via Local Training Data Statistics](https://mlsanity.com/paper/2603.11307/) (2026)

## 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.