Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning
Journal:
arXiv
Published Date:
Mar 25, 2025
Abstract
Federated continual learning (FCL) offers an emerging pattern to facilitate
the applicability of federated learning (FL) in real-world scenarios, where
tasks evolve dynamically and asynchronously across clients, especially in
medical scenario. Existing server-side FCL methods in nature domain construct a
continually learnable server model by client aggregation on all-involved tasks.
However, they are challenged by: (1) Catastrophic forgetting for previously
learned tasks, leading to error accumulation in server model, making it
difficult to sustain comprehensive knowledge across all tasks. (2) Biased
optimization due to asynchronous tasks handled across different clients,
leading to the collision of optimization targets of different clients at the
same time steps. In this work, we take the first step to propose a novel
server-side FCL pattern in medical domain, Dynamic Allocation Hypernetwork with
adaptive model recalibration (FedDAH). It is to facilitate collaborative
learning under the distinct and dynamic task streams across clients. To
alleviate the catastrophic forgetting, we propose a dynamic allocation
hypernetwork (DAHyper) where a continually updated hypernetwork is designed to
manage the mapping between task identities and their associated model
parameters, enabling the dynamic allocation of the model across clients. For
the biased optimization, we introduce a novel adaptive model recalibration
(AMR) to incorporate the candidate changes of historical models into current
server updates, and assign weights to identical tasks across different time
steps based on the similarity for continual optimization. Extensive experiments
on the AMOS dataset demonstrate the superiority of our FedDAH to other FCL
methods on sites with different task streams. The code is
available:https://github.com/jinlab-imvr/FedDAH.
