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...

Program Matematika Indonesia v0.63.20 — D110 English Mirror + A10 Adapter

Start learning / Mulai belajar English — open the learner curriculum · Bahasa Indonesia — buka peta belajar This release adds the complete program-hosted original-English D110 Mathematics in Lean mirror while keeping the authoritative upstream source separately prominent. Every centrally hosted page returns to both loc...

Privacy-Preserving Federated Learning for Container Kubernetes Orchestration in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a compelling architecture for training machine learning models across distributed Kubernetes clusters without centralising sensitive data, yet its practical deployment in resource-constrained settings such as Uganda confronts two interrelated obstacles that current literature treats in isolati...

Distributed Learning with Differential Privacy for Federated Edge Computing

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources, primarily located at edge devices. However, the inherent distributed nature of FL introduces significant challenges, particularly regarding privacy vulnerabilities and substantial communication overhea...

Adversarial Training for Secure Federated Learning with Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, this paradigm is increasingly vulnerable to adversarial attacks, where malicious participants can manipulate the training process to infer sensitive informatio...

Algorithmic Bias Detection in Federated Learning

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources, preserving data privacy. However, the inherent data heterogeneity across clients introduces a significant challenge: algorithmic bias. This paper presents a novel method for detecting and quantifying b...

MHAT-FL: An Open-Source TensorFlow Framework for Federated Human Activity Recognition with Attention-Matrix Positional Encoding

Human Activity Recognition (HAR) systems deployed on wearable and edge devices continuously collect sensitive user data, raising critical privacy concerns and communication bottlenecks. While Federated Learning (FL) mitigates these issues by decentralizing model training and keeping raw data on-device, deploying highly...

Adversarial Training for Secure Federated Learning with Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, this paradigm is increasingly vulnerable to adversarial attacks, where malicious participants can manipulate the training process to infer sensitive informatio...