Customizing Federated Learning to the Edge Device (Venkatesh Saligrama, Boston University)

We propose a novel method for federated learning that is customized to the objective of a given edge device. In our proposed method, a server trains a global meta-model by collaborating with devices without actually sharing data. The trained global meta-model is then customized locally by each device to meet its specific objective. Different from the conventional federated learning setting, training customized models for each device is hindered by both the inherent data biases of the various devices, as well as the requirements imposed by the federated architecture. We present an algorithm that locally de-biases model updates, while leveraging distributed data, so that each device can be effectively customized towards its objectives. Our method is fully agnostic to device heterogeneity and imbalanced data, scalable to massive number of devices, and allows for arbitrary partial participation. Our method has built-in convergence guarantees, and on benchmark datasets we demonstrate that it outperforms other state-of-art methods.

Biography Venkatesh Saligrama is a faculty member in the Department of Electrical and Computer Engineering, the Department of Computer Science (by courtesy), and a founding member of the Faculty of Computing and Data Sciences at Boston University. He holds a PhD from MIT. His research interests are broadly in the area of Artificial Intelligence, and his recent work has focused on machine learning with resource-constraints. He is an IEEE Fellow and recipient of several awards including Distinguished Lecturer for IEEE Signal Processing Society, the Presidential Early Career Award (PECASE), ONR Young Investigator Award, the NSF Career Award. More information about his work is available at http://sites.bu.edu/data