federated learning

PhysGuard-FL: Reproducibility package for physics-aware Byzantine-robust federated learning in chemical process monitoring

Complete research compendium for PhysGuard-FL, a physics-aware Byzantine-robust federated-learning framework evaluated for chemical-process monitoring. The frozen benchmark uses six Tennessee Eastman Process operating modes, 20 federated clients, 20 communication rounds, five local steps, and 10 matched random seeds. T...

Program Matematika Indonesia v0.63.24 - A30, B95, and C140 Modular Adapters

Start learning: Bahasa Indonesia · English interface. Version 0.63.24 preserves reciprocal learner navigation across the complete hosted program surface: every reader links to its course and localized program, every course links to its readers, and hosted copies retain prominent authoritative-original routes. Current l...

Secure federated large language model-enhanced multimodal visual communication framework

Mechanism Design for a Sustainable Federated Session Recommender System

FedPerKD privacy aware personalized federated learning with knowledge distillation for edge oriented tomato leaf disease detection

FedPer-KD, a privacy-aware federated framework that combines personalization with client-side knowledge distillation, is presented, which is effective under mild client heterogeneity, and is edge-oriented for downstream deployment.

Towards Explaining Backdoor Durability in Federated Learning via Influence Functions

QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning

Mutual Head Distillation for Structured Personalization in Federated Learning

COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication

Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning

A Review of Lightweight Federated Learning Research for Home Edge IoT

A co-optimization research framework is proposed, incorporating pruning rate, quantization bit width, and aggregation weights into a unified optimization space, to strike a balance among global model accuracy, training energy consumption, and convergence speed under device resource constraints.

History-Aware Multi-Objective Client Selection for Federated Learning under Statistical and System Heterogeneity

Federated learning reduces raw-data movement, but partial participation under statistical and system heterogeneity makes the selected client cohort a major source of optimisation bias and delay. This study proposes a history-aware multi-objective selector that combines opt-in data-quality metadata, exponentially smooth...