Stability-aware multi-objective federated learning via deep reinforcement learning: Client selection and resource optimization in dynamic edge networks

A federated class-incremental learning framework with dynamic client participation and evolution for machine fault diagnosis

A hierarchical federated learning approach based on cloud-fog-edge computing architecture for distributed smart manufacturing systems

LCSNet–FedPerGC: An efficient federated learning framework for rice leaf disease classification in the Mekong Delta

A Decentralized Learning Architecture for Medical Prescription Anomaly Detection via Hybrid Federated-Swarm Learning

The increasing use of electronic medical records (EMRs) has improved efficiency, accuracy, and accessibility of patient data. However, conventional centralized architectures suffer from single points of failure and data privacy issues. To address these challenges, this study proposes a decentralized machine learning architecture that combines concepts from Federated Learning (FL) and Swarm Learning (SL) for anomaly detection in medical prescriptions. The proposed architecture leverages blockchain and the InterPlanetary File System (IPFS) to enable secure model sharing and decentralized storage, thereby reducing communication complexity and establishing a transparent, decentralized parameter repository. Experimental evaluations were conducted using logistic regression (LR), a multi-layer perceptron (MLP), and a decision tree (DT) model. Compared with the FL baseline, the proposed system achieved superior efficiency, lower resource consumption, and improved latency, along with smaller block sizes. It, however, exhibited slightly lower transaction throughput and longer training rounds, reflecting the added complexity of decentralization. In predictive performance on the anomaly classification task, DT achieved the highest precision and recall under the evaluated dataset (F1-score=0.9912), followed by MLP (0.5504) and LR (0.2442). The decentralized training approach led to negligible performance loss relative to centralized models, less than 4% for LR and below 1% for both MLP and DT. Overall, the proposed system demonstrates a robust and efficient alternative for decentralized learning in healthcare applications, maintaining strong predictive performance while enhancing architectural transparency.

A multi-agent deep reinforcement learning framework for coordinated urban traffic signal and parking lot exit control

Extension-layer fine-tuning for transfer learning in multi-agent reinforcement learning-based HVAC control