Federated Learning Enhancement Through Transfer and Continual Learning Integration: Analyzing Effects of Different Levels of Dirichlet Distribution

Machine learning plays a pivotal role in modern technology, driving advancements across various domains such as healthcare, finance, and autonomous systems. Federated Learning (FL) offers a significant advantage over traditional machine learning by enabling decentralized model training without requiring data to be centralized, thereby enhancing privacy and security. With the advent of 6G networks, which promise ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC), FL can be significantly enhanced. 6G’s improved bandwidth and latency characteristics will enable more efficient data exchange and model updates, further enhancing the adoption of FL. However, the performance of FL can be significantly affected by data distribution, particularly in non-IID (non-Independent and Identically Distributed) scenarios, where FL tends to perform poorly. This paper proposes a novel approach to enhance FL by integrating Transfer Learning (TL) and Continual Learning (CL), named Integrated Federated Transfer and Continual Learning (IFTCL). TL can extract features from client training samples to benefit subsequent clients, while CL mitigates catastrophic forgetting caused by heterogeneous data across clients. This integration improves FL performance under varying degrees of heterogeneous data distributions simulated by Dirichlet distribution, enhancing accuracy, convergence speed, and reducing communication overhead. The proposed method’s feasibility is validated using a publicly available radar recognition dataset.