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

https://doi.org/10.1145/3678182
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FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization
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Federated Adam-Type Algorithm for Distributed Optimization With Lazy Strategy
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FedAB: Truthful Federated Learning With Auction-Based Combinatorial Multi-Armed Bandit
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Client Selection in Federated Learning: Principles, Challenges, and Opportunities
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Scheduling and Aggregation Design for Asynchronous Federated Learning Over Wireless Networks
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Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
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Endogenous Security Defense against Deductive Attack: When Artificial Intelligence Meets Active Defense for Online Service
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Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions
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The Roadmap to 6G: AI Empowered Wireless Networks
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Model-Agnostic Meta-Learning Techniques: A State-of-The-Art Short Review
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Federated Learning: Challenges, Methods, and Future Directions
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Decentralized P2P Federated Learning for Privacy-Preserving and Resilient Mobile Robotic Systems
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HFedMTL: Hierarchical Federated Multi-Task Learning
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Towards secure and reliable aggregation for Federated Learning protocols in healthcare applications
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Exploiting Unintended Feature Leakage in Collaborative Learning
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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
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CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks
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Never Too Late: Tracing and Mitigating Backdoor Attacks in Federated Learning
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Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
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Personalized Federated Learning With Adaptive Batchnorm for Healthcare
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Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies
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Fast-Convergent Federated Learning With Adaptive Weighting
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Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges
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Model Compression via Pattern Shared Sparsification in Analog Federated Learning Under Communication Constraints
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ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning
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Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT
inferredcites →https://doi.org/10.1109/tii.2021.3095506openalex-snapshot
Secure and Efficient Federated Learning for Smart Grid With Edge-Cloud Collaboration
inferredcites →https://doi.org/10.1109/tii.2021.3132954openalex-snapshot
RobustFL: Robust Federated Learning Against Poisoning Attacks in Industrial IoT Systems
inferredcites →https://doi.org/10.1109/tii.2022.3168011openalex-snapshot
Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-Based IIoT Networks
inferredcites →https://doi.org/10.1109/tii.2022.3183465openalex-snapshot
Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT
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A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
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Learned Gradient Compression for Distributed Deep Learning
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Towards Personalized Federated Learning
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Privacy and Robustness in Federated Learning: Attacks and Defenses
inferredcites →https://doi.org/10.1109/tnnls.2022.3225715openalex-snapshot
Federated Learning via Plurality Vote
inferredcites →https://doi.org/10.1109/tnnls.2023.3294295openalex-snapshot
Active Client Selection for Clustered Federated Learning
inferredcites →https://doi.org/10.1109/tnse.2021.3050781openalex-snapshot
VFChain: Enabling Verifiable and Auditable Federated Learning via Blockchain Systems
inferredcites →https://doi.org/10.1109/tnse.2021.3053588openalex-snapshot
Optimal User-Edge Assignment in Hierarchical Federated Learning Based on Statistical Properties and Network Topology Constraints
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Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System
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Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning
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FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders
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Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation
inferredcites →https://doi.org/10.1109/tpds.2022.3206480openalex-snapshot
Federated Learning With Nesterov Accelerated Gradient
inferredcites →https://doi.org/10.1109/tpds.2022.3230840openalex-snapshot
Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning
inferredcites →https://doi.org/10.1109/tpds.2023.3240767openalex-snapshot
Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems
inferredcites →https://doi.org/10.1109/tpds.2023.3267897openalex-snapshot
Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation
inferredcites →https://doi.org/10.1109/tpds.2023.3273490openalex-snapshot
Securing Distributed SGD Against Gradient Leakage Threats
inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2019.00057openalex-snapshot
Poisoning Attack in Federated Learning using Generative Adversarial Nets
inferredcites →https://doi.org/10.1109/tsp.2022.3153135openalex-snapshot
Robust Aggregation for Federated Learning
inferredcites →https://doi.org/10.1109/tsp.2023.3244092openalex-snapshot
Joint Privacy Enhancement and Quantization in Federated Learning
inferredcites →https://doi.org/10.1109/tvt.2020.2973651openalex-snapshot
Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles
inferredcites →https://doi.org/10.1109/twc.2022.3190512openalex-snapshot
Hierarchical Federated Learning With Quantization: Convergence Analysis and System Design
inferredcites →https://doi.org/10.1109/twc.2022.3232891openalex-snapshot
Efficient Client Selection Based on Contextual Combinatorial Multi-Arm Bandits
inferredcites →https://doi.org/10.1109/twc.2023.3315962openalex-snapshot
Over-The-Air Federated Learning Over Scalable Cell-Free Massive MIMO
inferredcites →https://doi.org/10.1109/wcnc51071.2022.9771619openalex-snapshot
Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey
inferredcites →https://doi.org/10.1117/12.2611654openalex-snapshot
Communication-efficient federated learning for multi-institutional medical image classification
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3133956.3134012openalex-snapshot
Deep Models Under the GAN
inferredcites →https://doi.org/10.1145/3412357openalex-snapshot
Federated Learning in a Medical Context: A Systematic Literature Review
inferredcites →https://doi.org/10.1145/3447867openalex-snapshot
Hardware Information Flow Tracking
inferredcites →https://doi.org/10.1145/3458864.3466628openalex-snapshot
PPFL
inferredcites →https://doi.org/10.1145/3460427openalex-snapshot
A Comprehensive Survey of Privacy-preserving Federated Learning
inferredcites →https://doi.org/10.1145/3501296openalex-snapshot
Federated Learning for Smart Healthcare: A Survey
inferredcites →https://doi.org/10.1145/3543873.3587681openalex-snapshot
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy
inferredcites →https://doi.org/10.1145/3550289openalex-snapshot
FLAME
inferredcites →https://doi.org/10.1145/3560486openalex-snapshot
ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences
inferredcites →https://doi.org/10.1145/3591354openalex-snapshot
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph
inferredcites →https://doi.org/10.1145/3659943openalex-snapshot
Meta-learning Approaches for Few-Shot Learning: A Survey of Recent Advances
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Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
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Advances and Open Problems in Federated Learning
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Personalized Cross-Silo Federated Learning on Non-IID Data
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Artificial intelligence and statistics
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Federated learning for 6G communications: Challenges, methods, and future directions
inferredcites →https://doi.org/10.23919/jcin.2021.9663107openalex-snapshot
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation
inferredcites →https://doi.org/10.3390/e24050686openalex-snapshot
Robust Aggregation for Federated Learning by Minimum γ-Divergence Estimation
inferredcites →https://doi.org/10.3390/en16052486openalex-snapshot
Federated-Learning-Based Energy-Efficient Load Balancing for UAV-Enabled MEC System in Vehicular Networks
inferredcites →https://doi.org/10.3390/s22103728openalex-snapshot
FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information
inferredcites →https://doi.org/10.3390/s22124394openalex-snapshot
Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
inferredcites →https://doi.org/10.3390/s22187061openalex-snapshot
A Cluster-Driven Adaptive Training Approach for Federated Learning
inferredcites →https://doi.org/10.3390/s23031052openalex-snapshot
Edge-Cloud Collaborative Defense against Backdoor Attacks in Federated Learning
inferredcites →https://doi.org/10.3390/s23042093openalex-snapshot
A Federated Learning Multi-Task Scheduling Mechanism Based on Trusted Computing Sandbox
inferredcites →https://doi.org/10.3934/mbe.2023475openalex-snapshot
Personalized federated learning for heterogeneous data: A distributed edge clustering approach
inferredcites →https://doi.org/10.4236/jis.2023.142008openalex-snapshot
A Systematic Survey for Differential Privacy Techniques in Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.2003.14053openalex-snapshot
Inverting Gradients -- How easy is it to break privacy in federated learning?
inferredcites →https://doi.org/10.48550/arxiv.2010.12229openalex-snapshot
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.2106.10435openalex-snapshot
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.2107.11621openalex-snapshot
FedLab: A Flexible Federated Learning Framework
inferredcites →https://doi.org/10.48550/arxiv.2207.00428openalex-snapshot
FLVoogd: Robust And Privacy Preserving Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.2210.16105openalex-snapshot
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
inferredcites →https://doi.org/10.5555/1953048.2021068openalex-snapshot
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1109/icicse66971.2025.11429888openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icton71309.2026.11704924openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11228967openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/jiot.2026.3671102openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tce.2026.3681623openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tgcn.2026.3711489openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/wcnc61545.2025.10978709openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/wcnc61545.2025.10978723openalex-snapshot
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inferred← citeshttps://doi.org/10.12688/openreseurope.24355.1openalex-snapshot
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inferred← citeshttps://doi.org/10.2139/ssrn.6257374openalex-snapshot
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inferred← citeshttps://doi.org/10.23919/softcom66362.2025.11197429openalex-snapshot
Privacy-Preserving Federated Data Governance Framework for Secure Parameter Exchange in Distance Education
inferred← citeshttps://doi.org/10.3389/fmicb.2025.1738926openalex-snapshot
Applications and prospects of artificial intelligence in the auxiliary diagnosis of pediatric pulmonary tuberculosis
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inferred← citeshttps://doi.org/10.3390/s26103198openalex-snapshot
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Using Federated Learning to Develop Congestion Control Solutions for Intelligent Transportation Systems
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