federated learning

Rusltareq/FedGov-IoT-IDS: Initial public release

Initial public release of the code accompanying this PhD research: a federated learning (FedAvg/FedProx/FedDyn) framework for IoT intrusion detection, and the ReputationManager smart contract for active on-chain governance.

Wireless Sensor Network Intrusion Detection: A Review of Statistical, Machine Learning, and Federated Methods

Wireless Sensor Networks have become essential infrastructure across environmental monitoring, healthcare, smart agriculture, industrial automation, and smart city applications. However, their distributed, resource-constrained, and physically accessible design creates serious security risks. This paper surveys cybersec...

Robust federated learning under data heterogeneity and Byzantine attacks with adaptive compression

FL-HeteroSecure consistently outperforms state-of-the-art baselines, including FedGAN, FMDS-FL, and HFMDS-FL, improving accuracy, accelerating convergence, and reducing communication costs.

Efficient and explainable intrusion detection for the internet of things using modified-LSTM and HTM-inspired sparse CNN with federated learning

According to the World Economic Forum, IoT devices have become a main target for Dark Web attacks. There is therefore a pressing need to develop intrusion detection systems suitable for deployment on IoT devices. To this end, we developed an IDS inspired from Hierarchical Temporal Memory (HTM) with Sparse CNN model tha...

Privacy-Preserving Healthcare Diagnostics: A Comparative Study of Horizontal and Vertical Federated Learning Approaches

Federated learning (FL) is a novel method of healthcare diagnostics that allows training cross -institutional collaborative models without violating data privacy. This paper will compare and contrast the two approaches of vertical and horizontal federated learning to detect diseases such as pneumonia based on the use o...

Deep learning models for bone fracture detection from current trends and challenges to future directions

Bone fractures pose a significant global health concern. Accurate and prompt diagnosis of these injuries is necessary to prevent serious complications. Recent advancements in deep learning enable automated fracture detection, which can assist radiologists. This review provides an overview of deep learning application...

Federated Learning: Architectures, Challenges, and Applications in Distribute Machine Learning Intelligence

Federated Learning (FL) is a recently emerging machine learning model proposed to solve the issue of ownership, privacy, and security in collaborative model training. With FL, unlike the traditional centralized learning, raw data transfer to a central server is not required, and several devices or nodes can locally tra...

Privacy Preserving Disease Prediction Across Multiple Hospitals Using CKKS Based Homomorphic Federated Learning

AI-driven risk assessment models for predicting healthcare cybersecurity threats in real-time

3D Coherent imaging with machine learning for rapid identification of microplastics

Fractal-Aware Federated AI for Personalized Demand-Side Management: A Review of Household Heterogeneity, Privacy, and Flexibility Learning

Federated artificial intelligence is increasingly viewed as a promising approach for privacy-preserving demand-side management because it enables distributed learning from household energy data without centralizing raw smart-meter records. However, existing federated energy models often treat household heterogeneity ma...

Fair2Zero: Fairness-Aware Aggregation in Federated Learning