Overcoming Forgetting in Federated Learning on Non-IID Data
This addresses the challenge of maintaining model consistency in distributed settings for applications like privacy-sensitive data analysis, though it is incremental as it builds on existing techniques.
The paper tackled the problem of model drift in Federated Learning with non-IID data by adapting a Lifelong Learning solution to add a penalty term, showing superior performance on MNIST image recognition compared to competing methods.
We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling all local models to converge to a shared optimum. We show that this can be done efficiently for communication (adding no further privacy risks), scaling with the number of nodes in the distributed setting. Our experiments show that this method is superior to competing ones for image recognition on the MNIST dataset.