Cover Image

Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ranaweera, Kanishka, Neiat, Azadeh Ghari, Liu, Xiao, Kashyap, Bipasha, Pathirana, Pubudu N.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning
by: Ranaweera, Kanishka, et al.
Published: (2025)

Federated Learning with Differential Privacy: An Utility-Enhanced Approach
by: Ranaweera, Kanishka, et al.
Published: (2025)

Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data
by: Ranaweera, Kanishka, et al.
Published: (2025)

Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications
by: Tran, Ha, et al.
Published: (2026)

StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting
by: Quan, Minh K., et al.
Published: (2026)

VR Based Emotion Recognition Using Deep Multimodal Fusion With Biosignals Across Multiple Anatomical Domains
by: Indrasiri, Pubudu L., et al.
Published: (2024)

Blockchain-Enabled Device-Enhanced Multi-Access Edge Computing in Open Adversarial Environments
by: Islam, Muhammad, et al.
Published: (2024)

Geometric Analysis of Speech Representation Spaces: Topological Disentanglement and Confound Detection
by: Kashyap, Bipasha, et al.
Published: (2026)

Leveraging Vision Transformers for Enhanced Classification of Emotions using ECG Signals
by: Indrasiri, Pubudu L., et al.
Published: (2025)

Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments
by: Quan, Minh K., et al.
Published: (2025)

An Interpretable Client Decision Tree Aggregation process for Federated Learning
by: Argente-Garrido, Alberto, et al.
Published: (2024)

Coding-Enforced Resilient and Secure Aggregation for Hierarchical Federated Learning
by: Weng, Shudi, et al.
Published: (2026)

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

Quantum Approaches for Dysphonia Assessment in Small Speech Datasets
by: Tran, Ha, et al.
Published: (2025)

Byzantine-Robust Aggregation for Securing Decentralized Federated Learning
by: Cajaraville-Aboy, Diego, et al.
Published: (2024)

Quantifying Dimensional Independence in Speech: An Information-Theoretic Framework for Disentangled Representation Learning
by: Kashyap, Bipasha, et al.
Published: (2026)

FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)

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

Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
by: Weng, Shudi, et al.
Published: (2025)

HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
by: Chen, Qiyuan, et al.
Published: (2026)

FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
by: Shen, Leming, et al.
Published: (2025)

Over-the-Air Federated Learning via Weighted Aggregation
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)

Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients
by: Wu, Leming, et al.
Published: (2025)

Federated Learning for Cyber Physical Systems: A Comprehensive Survey
by: Quan, Minh K., et al.
Published: (2025)

Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation
by: Guan, Zenghao, et al.
Published: (2025)

Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning
by: Wang, Kun, et al.
Published: (2024)

Communication-Efficient Federated Learning with Accelerated Client Gradient
by: Kim, Geeho, et al.
Published: (2022)

Federated Learning with Sample-level Client Drift Mitigation
by: Xu, Haoran, et al.
Published: (2025)

Unlearning Clients, Features and Samples in Vertical Federated Learning
by: Varshney, Ayush K., et al.
Published: (2025)

Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networks
by: Quan, Minh K., et al.
Published: (2025)

Certified Causal Attribution for Real-Time Attack Forensics in 6G Network Slicing
by: Quan, Minh K., et al.
Published: (2026)

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
by: Jiang, Xuefeng, et al.
Published: (2024)

Enhancing Smart Farming Through Federated Learning: A Secure, Scalable, and Efficient Approach for AI-Driven Agriculture
by: Janga, Ritesh, et al.
Published: (2025)

FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering
by: Ping, Siqi, et al.
Published: (2024)

Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection
by: HaghighiFard, M. Saeid, et al.
Published: (2024)

FedZMG: Efficient Client-Side Optimization in Federated Learning
by: Zantalis, Fotios, et al.
Published: (2026)

Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation
by: Elbatel, Marawan, et al.
Published: (2024)

FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
by: Fan, Boyu, et al.
Published: (2024)

Federated Learning With Individualized Privacy Through Client Sampling
by: Lange, Lucas, et al.
Published: (2025)

Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
by: Pérez-Corral, Cristian, et al.
Published: (2026)