Federated Residual Learning
This addresses data heterogeneity issues in federated learning, which is incremental as it builds on existing federated learning methods.
The paper tackles the problem of slow convergence in federated learning with non-i.i.d. data by proposing a framework where clients train personalized local models and make predictions jointly with a server-side shared model, achieving substantial performance gains over baselines.
We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides. Our framework is robust to data heterogeneity, addressing the slow convergence problem traditional federated learning methods face when the data is non-i.i.d. across clients. We test the theory empirically and find substantial performance gains over baselines.