federated learning

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.

Unlocking Federated Learning for ESG Reporting: Prioritizing Critical Adoption Challenges in an Emerging Economy Context

ABSTRACT The growing focus on environmental, social, and governance (ESG) issues has prompted organizations to explore innovative ways to manage their businesses. It has become imperative for organizations to adopt ESG reporting. The applications of federated learning help to enhance the scalability and credibility of...

FairPFL

FairPFL is a federated learning framework for IoT intrusion detection that treats fairness as an operational requirement: no device population should be left systematically under-protected by the shared detector. It combines a self-correcting fairness controller in the aggregation rule, a split personalization architec...

Trustworthy 6G Networks: Blockchain-Based Federated Learning with Deep Spinal Forward Harmonic Net

Adaptive intelligence in smart microgrids: a systematic review and comparative analysis of RL-based control, communication, and simulation frameworks

Abstract This article is framed as a systematic review with original illustrative benchmarking of reinforcement learning (RL) for smart microgrid (SMG) control, communication, and simulation-to-real deployment. Although prior surveys have reviewed RL for microgrid energy management, they have not jointly analyzed contr...

Artificial Intelligence-Enabled Battery Energy Storage Systems for Renewable Energy: A Comprehensive Review of Technologies, Applications, Challenges, and Future Directions

The rapid growth of renewable energy sources, particularly solar and wind power, has increased the demand for efficient and reliable battery energy storage systems (BESSs) to address intermittency, enhance grid stability, and improve energy management. In recent years, artificial intelligence (AI) has emerged as a tran...

SECURE FEDERATED INTRUSION DETECTION USING HOMOMORPHIC ENCRYPTION: A COMPARATIVE STUDY WITH ENSEMBLE LEARNING

The rapid growth of the Internet of Things (IoT) has also resulted in the increase of the demand in the intrusion detection systems, which can detect suspicious activity and keep the information confidential. The traditional centralized machine learning systems involve attaching the data of the distributed devices to a...