Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging

Problem
This paper addresses the limitations of existing Federated Unlearning (FU) methods, which predominantly rely on synchronous coordination. Such methods necessitate halting the entire federated learning process to accommodate stragglers, leading to significant delays, particularly in heterogeneous device environments. Additionally, current FU techniques often fail to achieve genuine data erasure, as the influence of removed data can resurface in subsequent training iterations. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose Asynchronous Federated Unlearning with Invariance Calibration (AFU-IC), a framework specifically designed for medical imaging applications. AFU-IC decouples the unlearning process from the global training workflow, allowing target clients to perform unlearning operations asynchronously. This is achieved through a server-side invariance calibration mechanism that ensures the model does not relearn the erased data. The architecture leverages a federated learning setup where clients can independently manage their data contributions while maintaining compliance with data protection regulations. The paper does not disclose specific details regarding the loss functions, training compute, or the exact architecture used, focusing instead on the operational framework and its implications.

Results
AFU-IC was evaluated on three medical imaging benchmarks, demonstrating unlearning efficacy and model fidelity comparable to traditional retraining methods. The results indicate that AFU-IC significantly reduces wall-clock latency compared to synchronous FU baselines. While specific numerical results are not provided in the abstract, the authors claim that the proposed method achieves a balance between compliance and performance, suggesting a substantial improvement in efficiency over existing methods.

Limitations
The authors acknowledge that while AFU-IC improves upon synchronous FU methods, it may still face challenges related to the inherent complexities of medical data and the potential for incomplete data erasure in certain scenarios. They do not discuss the scalability of the approach in larger federated networks or the potential computational overhead introduced by the invariance calibration mechanism. Additionally, the reliance on asynchronous operations may introduce new synchronization issues that are not fully addressed.

Why it matters
The implications of AFU-IC are significant for the deployment of federated learning in sensitive domains such as healthcare, where data privacy is paramount. By enabling asynchronous unlearning, the framework allows for more efficient model updates without compromising compliance with data protection regulations. This advancement could facilitate broader adoption of federated learning in medical imaging and other fields requiring stringent data governance, ultimately enhancing the robustness and reliability of machine learning models in real-world applications.

Authors: Zhaoyuan Cai, Xinglin Zhang
Source: arXiv:2604.26809
URL: https://arxiv.org/abs/2604.26809v1