Cover Image

FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Haque, Md Rafid, Kamal, Abu Raihan Mostofa, Hossain, Md. Azam
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing I.2.11; C.2.4; K.6.5
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks
by: Dong, Fan, et al.
Published: (2023)

How Machine Learning-Data Driven Replication Strategies Enhance Fault Tolerance in Large-Scale Distributed Systems
by: Murimi, Almond Kiruthu
Published: (2025)

HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregation
by: Nguyen, Thinh, et al.
Published: (2025)

Accelerating Geo-distributed Machine Learning with Network-Aware Adaptive Tree and Auxiliary Route
by: Li, Zonghang, et al.
Published: (2024)

AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments
by: Zhang, Guilin, et al.
Published: (2025)

Impact of Network Topology on Byzantine Resilience in Decentralized Federated Learning
by: Bhattacharya, Siddhartha, et al.
Published: (2024)

Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism
by: Saadati, Yasaman, et al.
Published: (2024)

Complex Event Processing in the Edge: A Combined Optimization Approach for Data and Code Placement
by: Uyanık, Halit, et al.
Published: (2026)

The Bureaucracy of Speed: Structural Equivalence Between Memory Consistency Models and Multi-Agent Authorization Revocation
by: Parakhin, Vladyslav
Published: (2026)

Agent Identity URI Scheme: Topology-Independent Naming and Capability-Based Discovery for Multi-Agent Systems
by: Rodriguez Jr, Roland R.
Published: (2026)

Asynchronous Byzantine Federated Learning
by: Cox, Bart, et al.
Published: (2024)

Training Diffusion Models with Federated Learning
by: de Goede, Matthijs, et al.
Published: (2024)

Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments
by: Zhang, Guilin, et al.
Published: (2025)

Aergia: Leveraging Heterogeneity in Federated Learning Systems
by: Cox, Bart, et al.
Published: (2022)

Parameterizing Federated Continual Learning for Reproducible Research
by: Cox, Bart, et al.
Published: (2024)

A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
by: Rosendal, Daan, et al.
Published: (2026)

FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
by: Woisetschläger, Herbert, et al.
Published: (2023)

Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
by: Zuo, Yuncong, et al.
Published: (2024)

Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
by: Zhang, Haoran, et al.
Published: (2025)

FedPLT: Scalable, Resource-Efficient, and Heterogeneity-Aware Federated Learning via Partial Layer Training
by: Dabaja, Ahmad, et al.
Published: (2026)

Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning
by: Petrella, Alfredo, et al.
Published: (2023)

Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models
by: Niu, Ziru, et al.
Published: (2025)

A Framework for testing Federated Learning algorithms using an edge-like environment
by: Schwanck, Felipe Machado, et al.
Published: (2024)

A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments
by: Colybes, Elouan, et al.
Published: (2026)

From Logic Monopoly to Social Contract: Separation of Power and the Institutional Foundations for Autonomous Agent Economies
by: Ruan, Anbang
Published: (2026)

Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes
by: Motta, Steven, et al.
Published: (2026)

Designing Scalable Rate Limiting Systems: Algorithms, Architecture, and Distributed Solutions
by: Guan, Bo
Published: (2026)

SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation
by: Zhang, Zhuosheng, et al.
Published: (2021)

TAGC: Optimizing Gradient Communication in Distributed Transformer Training
by: Polyakov, Igor, et al.
Published: (2025)

ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuning
by: Wang, Xiaoyu, et al.
Published: (2025)

Federated Learning with Differential Privacy
by: Banse, Adrien, et al.
Published: (2024)

SI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data Sharing
by: Zhao, Mingjie, et al.
Published: (2026)

Faramesh: A Protocol-Agnostic Execution Control Plane for Autonomous Agent Systems
by: Fatmi, Amjad
Published: (2026)

Comparison of Autoscaling Frameworks for Containerised Machine-Learning-Applications in a Local and Cloud Environment
by: Schroeder, Christian, et al.
Published: (2023)

SPEAR:Exact Gradient Inversion of Batches in Federated Learning
by: Dimitrov, Dimitar I., et al.
Published: (2024)

Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
by: Woisetschläger, Herbert, et al.
Published: (2023)

A Survey on Efficient Federated Learning Methods for Foundation Model Training
by: Woisetschläger, Herbert, et al.
Published: (2024)

AutoDDL: Automatic Distributed Deep Learning with Near-Optimal Bandwidth Cost
by: Chen, Jinfan, et al.
Published: (2023)

Scheduling the Unschedulable: Taming Black-Box LLM Inference at Scale
by: Yuan, Renzhong, et al.
Published: (2026)

Roadmap for Edge AI: A Dagstuhl Perspective
by: Ding, Aaron Yi, et al.
Published: (2021)