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

2025-11-27

AI Summary

Federated learning (FL) confronts fundamental challenges including statistical heterogeneity, privacy preservation, and collaborative efficiency. To address these, this work establishes a hybrid research framework integrating bibliometric analysis and systematic review. It proposes the first multi-level taxonomy of aggregation techniques, structured along three core dimensions: personalization, optimization, and robustness. Through empirical evaluation, it systematically benchmarks mainstream FL architectures and synchronization strategies under both IID and non-IID data settings. Furthermore, it develops a reproducible benchmarking platform for aggregation methods. The study identifies critical technical bottlenecks and provides theoretically grounded guidance and practical pathways for emerging directions—including privacy-enhancing aggregation, heterogeneity-aware modeling, and robust aggregation. Collectively, this work significantly advances the rigor, reproducibility, and extensibility of systematic FL research.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Surveying federated learning aggregation techniques and challenges
• Addressing data privacy, heterogeneity, and efficiency in decentralized AI
• Providing taxonomy and experiments for future FL research directions

Innovation

• Federated Learning enables decentralized collaborative model training
• Survey uses hybrid bibliometric analysis and systematic review methodology
• Aggregation strategies address heterogeneity, efficiency, security, and privacy