Abstract
The growing demand for privacy-preserving machine learning has driven the advancement of federated learning (FL), facilitating decentralized model training without revealing raw data. Nevertheless, conventional FL methods frequently encounter challenges in achieving an optimal balance between privacy, efficiency, and accuracy. To address these challenges, this study introduces the hybrid adaptive learning framework (HALF)—an innovative, scalable, and privacy-focused FL approach. HALF implements a multi-tiered hierarchical structure that includes local, regional, and global aggregation, combined with adaptive learning rates and integrated differential privacy (ε ≤ 2.3), ensuring robust privacy protection without sacrificing performance. The framework employs a structured five-phase process: Initialization, Device Selection, Training, Aggregation, and Evaluation, and incorporates lightweight algorithms with efficient communication protocols to reduce latency, energy consumption, and overhead. Experimental validation using benchmark datasets demonstrates that HALF consistently surpasses traditional FL by 15%–20% in accuracy while reducing communication overhead by 40%, maintaining ≥89.7% accuracy within 142 min of training. Its resilience under non-identical and independently distributed (non-IID) and heterogeneous data conditions, as well as its deployment with up to 10,000 clients, underscores its scalability. Relevant to critical sectors such as healthcare, Internet of Things (IoT), finance, and smart cities, HALF addresses existing FL limitations and establishes a foundation for secure, efficient, and adaptable AI solutions in sensitive data environments.