Improving Federated Transfer Learning for Different Levels of Non-IID Data with Multi-Round Transfer and Forgetting Mitigation

Federated Learning (FL) enhances data privacy by allowing clients to share only model parameters. However, it suffers from slow convergence speed and low accuracy under non-Independent and Identically Distributed (non-IID) data conditions due to the statistical heterogeneity across clients. To address this issue, Federated Transfer Learning (FTL) was introduced, which leverages pre-trained feature extraction layers from a subset of clients and transfers them to others, thereby improving convergence and reducing communication costs in lowly and moderately non-IID settings. Nevertheless, FTL remains ineffective under highly non-IID conditions, as the transferred representations struggle to generalize across diverse data patterns. In this paper, we systematically evaluate the performance of FL and FTL under varying non-IID levels and identify their limitations in extreme scenarios. We then propose two novel approaches: Multiple-round Federated Transfer Learning (MFTL) and Multiple-round Federated Transfer Continual Learning (MFTCL). MFTL employs iterative rounds of Transfer Learning (TL) to enhance robustness, while MFTCL integrates Continual Learning (CL) strategies by a replaying mechanism to mitigate catastrophic forgetting. Experimental results on a radar sensing image dataset, where varying numbers of clients are used during the pre-training phase to control feature learning under different levels of non-IID conditions, demonstrate that our methods outperform traditional FL and FTL in terms of convergence speed, accuracy and communication burden.