federated learning

Direction-aware local differential privacy for federated learning

FedDC: Efficient protection scheme based on chaotic system in federated learning

Data-free quantile signatures for secure federated learning in low-altitude intelligent transportation systems

VCRFL: A verifiable and collusion-resistant privacy-preserving framework for efficient federated learning

Secure and efficient federated learning using attribute-based homomorphic encryption

MetaCS-FL: A metaheuristic-based framework for client selection in federated learning systems

Expansion and constriction: A unified objective perspective for heterogeneous federated learning

Transformer-Driven Dual Attention Volatility Network Enhanced by Federated Learning for Lung Cancer Detection and Classification

A novel Federated Transformer-Based Dual Attention Volatility Network for lung cancer classification implements a decentralized federated learning architecture to ensure privacy-preserving model training over multiple clients.

A Survey on Foundation Models for Personalized Federated Intelligence

The rise of large language models (LLMs), such as ChatGPT, Gemini, and Grok, has reshaped the AI landscape. As prominent instances of foundational models (FMs), they exhibit remarkable capabilities in generating human-like content, pushing the boundaries towards artificial general intelligence (AGI). However, their lar...

A privacy-preserving federated learning framework for load forecasting in electrified intelligent transportation systems

The results show that privacy-preserving federated forecasting can support day-ahead charging coordination, depot energy management, and edge-cloud automation during the electrification of intelligent transportation systems.

Fed-XLM-R: a privacy-preserving federated framework with adapter-scoped differential privacy for mental health triage-level intent classification on resource-constrained edge devices

Overall, Fed-XLM-R demonstrates that privacy-preserving, communication-efficient, and edge-deployable federated NLP is achievable without substantial accuracy loss, offering a practical pathway toward privacy-compliant mental health triage systems pending real-world and multilingual validation.

Privacy-Aware and Resource-Efficient Split Learning for IoT Botnet Detection: A Multi-Dataset Experimental and Systems Evaluation

A traffic-constrained multi-client split-learning intrusion-detection system evaluated on BoT-IoT, N-BaIoT, and CIC-IDS2017, which identifies a practical accuracy–communication–energy–privacy operating point for constrained IoT clients.