Buchumschlag

A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Makris, Antonios, Dousis, Christos, Kritharakis, Emmanouil, Bouras, Stavros, Tserpes, Konstantinos
Format:	Preprint
Veröffentlicht:	2026
Schlagworte:	Machine Learning
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning
von: Kritharakis, Emmanouil, et al.
Veröffentlicht: (2025)

Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering
von: Kritharakis, Emmanouil, et al.
Veröffentlicht: (2025)

Enabling Adversarial Robustness in AI Models through Kubeflow MLOps
von: Bouras, Stavros, et al.
Veröffentlicht: (2026)

WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting
von: Theodoropoulos, Theodoros, et al.
Veröffentlicht: (2024)

Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement
von: Makris, Antonios, et al.
Veröffentlicht: (2024)

Privacy Evaluation of Generative Models for Trajectory Generation
von: Bouras, Stavros, et al.
Veröffentlicht: (2026)

Converter: A CEAML Reasoner Python package to Streamline Orchestration Across Cloud and Edge Continuum
von: Korontanis, Ioannis, et al.
Veröffentlicht: (2024)

A New Approach for Evaluating the Performance of Distributed Latency-Sensitive Services
von: Theodoropoulos, Theodoros, et al.
Veröffentlicht: (2024)

Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
von: Fang, Zihan, et al.
Veröffentlicht: (2026)

Fisher-Informed Parameterwise Aggregation for Federated Learning with Heterogeneous Data
von: Chang, Zhipeng, et al.
Veröffentlicht: (2026)

Enhancing Federated Learning with Kolmogorov-Arnold Networks: A Comparative Study Across Diverse Aggregation Strategies
von: Ma, Yizhou, et al.
Veröffentlicht: (2025)

Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data
von: Díaz, Judith Sáinz-Pardo, et al.
Veröffentlicht: (2025)

An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
von: Wang, Yuan, et al.
Veröffentlicht: (2024)

Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data
von: Peng, Jie, et al.
Veröffentlicht: (2024)

Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation
von: Ma, Xing
Veröffentlicht: (2025)

Learning Music Audio Representations With Limited Data
von: Plachouras, Christos, et al.
Veröffentlicht: (2025)

A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data
von: Tertulino, Rodrigo
Veröffentlicht: (2025)

On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond
von: Zeng, Dun, et al.
Veröffentlicht: (2023)

Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints
von: Ziliaskopoulos, Konstantinos, et al.
Veröffentlicht: (2026)

A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity
von: Erbani, Johan, et al.
Veröffentlicht: (2025)

Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
von: Chan, Yun-Hin, et al.
Veröffentlicht: (2023)

Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
von: Panagiotou, Emmanouil, et al.
Veröffentlicht: (2024)

Subspace Optimization for Efficient Federated Learning under Heterogeneous Data
von: Zhu, Shuchen, et al.
Veröffentlicht: (2026)

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures
von: Wang, Jiacheng, et al.
Veröffentlicht: (2025)

ILoRA: Federated Learning with Low-Rank Adaptation for Heterogeneous Client Aggregation
von: Zhou, Junchao, et al.
Veröffentlicht: (2025)

WinFLoRA: Incentivizing Client-Adaptive Aggregation in Federated LoRA under Privacy Heterogeneity
von: Kou, Mengsha, et al.
Veröffentlicht: (2026)

FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning
von: Sahoo, Pranab, et al.
Veröffentlicht: (2024)

Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks
von: Luqman, Alka, et al.
Veröffentlicht: (2024)

Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity
von: Chen, Xiaolu, et al.
Veröffentlicht: (2025)

StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
von: Bouzinis, Pavlos S., et al.
Veröffentlicht: (2024)

Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning
von: Ryum, Keumseo, et al.
Veröffentlicht: (2025)

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
von: Shao, Yumeng, et al.
Veröffentlicht: (2024)

FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning
von: Feng, Chao, et al.
Veröffentlicht: (2024)

Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
von: Zuo, Shiyuan, et al.
Veröffentlicht: (2024)

Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
von: Wang, Zijian, et al.
Veröffentlicht: (2025)

Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning
von: Egger, Maximilian, et al.
Veröffentlicht: (2025)

Fed-Meta-Align: A Similarity-Aware Aggregation and Personalization Pipeline for Federated TinyML on Heterogeneous Data
von: Macharla, Hemanth, et al.
Veröffentlicht: (2025)

Video Reconstruction using Diffusion-based Image-to-Video Generation with Trajectory Guidance
von: Bompai, Stelio, et al.
Veröffentlicht: (2026)

Federated Impression for Learning with Distributed Heterogeneous Data
von: Arya, Atrin, et al.
Veröffentlicht: (2024)

Fluid Democracy in Federated Data Aggregation
von: Kesari, Aditya Vema Reddy, et al.
Veröffentlicht: (2025)