FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation

Abstract

In federated healthcare systems, Federated Class-Incremental Learning (FCIL) has emerged as a key paradigm, enabling continuous adaptive model learning among distributed clients while safeguarding data privacy. However, in practical applications, data across agent nodes within the distributed framework often exhibits non-independent and identically distributed (non-IID) characteristics, rendering traditional continual learning methods inapplicable. To address these challenges, this paper covers more comprehensive incremental task scenarios and proposes a dynamic memory allocation strategy for exemplar storage based on the data replay mechanism. This strategy fully taps into the inherent potential of data heterogeneity, while taking into account the performance fairness of all participating clients, thereby establishing a balanced and adaptive solution to mitigate catastrophic forgetting. Unlike the fixed allocation of client exemplar memory, the proposed scheme emphasizes the rational allocation of limited storage resources among clients to improve model performance. Furthermore, extensive experiments are conducted on three medical image datasets, and the results demonstrate significant performance improvements compared to existing baseline models.

Figures (3)

• Figure 1: The legend reflects our motivations and contributions. The coordinate system is used to characterize the sample distribution of the client. Compared to common replay-based methods that allocate fixed memory size ${m_{fix}}$ per client and uniformly distribute it across all classes, FeDMRA leverages the heterogeneity of client data distributions and their performance contributions to dynamically allocate matched memory sizes ${m_{c}}$ and ${m_c^y}$, thereby optimizing the influence of stored exemplars.
• Figure 2: An overview of the proposed FeDMRA. Server: 1. Receive the model parameters $w_{c}$ and class distribution uploaded by the clients. 2. Calculate the storage size of example sets for each category on the client side through client-level and class-level calculations. 3. Aggregate the client models using FedAvg to update the global model. Client: 1. Filter samples to fill the allocated size ${m_c^y}$ of the example set and replay. 2. Use the global model $w_{{g,t}}$ as a teacher model to perform knowledge transfer for the local model. 3. Local incremental training updates the model and uploads it.
• Figure 3: Our method under different configuration (a) weight $\delta$ of $\mathcal{L}_{MG}$, (b) $m_{max}$ for FCIL, (c) $m_{max}$ for FDIL and FCDIL.