Unsupervised Federated Learning for Unbalanced Data

This work considers unsupervised learning tasks being implemented within the federated learning framework to satisfy stringent requirements for low-latency and privacy of the emerging applications. The proposed algorithm is based on Dual Averaging (DA), where the gradients of each agent are aggregated at a central node. While having its advantages in terms of distributed computation, the accuracy of federated learning training reduces significantly when the data is nonuniformly distributed across devices. Therefore, this work proposes two weight computation algorithms, with one using a fixed size bin and the other with self-organizing maps (SOM) that solves the underlying dimensionality problem inherent in the first method. Simulation results are also provided to show that the proposed algorithms' performance is comparable to the scenario in which all data is uploaded and processed in the centralized cloud.