Towards More Efficient Federated Learning with Better Optimization Objects
This work addresses data heterogeneity issues in federated learning for edge computing applications, but it is incremental as it builds on existing constraint-based methods like FedProx.
The paper tackles the problem of data heterogeneity slowing convergence and degrading performance in federated learning by proposing a method that uses the aggregation of all past models as a new constraint target, which experiments show significantly improves convergence speed and model performance.
Federated Learning (FL) is a privacy-protected machine learning paradigm that allows model to be trained directly at the edge without uploading data. One of the biggest challenges faced by FL in practical applications is the heterogeneity of edge node data, which will slow down the convergence speed and degrade the performance of the model. For the above problems, a representative solution is to add additional constraints in the local training, such as FedProx, FedCurv and FedCL. However, the above algorithms still have room for improvement. We propose to use the aggregation of all models obtained in the past as new constraint target to further improve the performance of such algorithms. Experiments in various settings demonstrate that our method significantly improves the convergence speed and performance of the model.