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IEEE Signal Processing Society Distinguished Lecture (“Personalizing Federated Learning to the Edge Device”)
November 30 @ 12:00 pm - 1:00 pm
IEEE SPS chapter of the Oregon section will host an SPS distinguished lecture by Prof. Venkatesh Saligrama (Boston University). The lecture title is “Personalizing Federated Learning to the Edge Device”, and the lecture is scheduled at noon-1pm PST on Tuesday, November 30. This event will be held via Zoom. When you register to the event, please enter your email address. We will send Zoom information to the registrants via email one day before the event. The talk abstract and the speaker bio are as follows: Title: Personalizing Federated Learning to the Edge Device Abstract: 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. Bio: 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 Virtual: https://events.vtools.ieee.org/m/291032