Federated Action Recognition on Heterogeneous Embedded Devices

• URL: http://arxiv.org/abs/2107.12147v1
• Date: Sun, 18 Jul 2021 02:33:24 GMT
• Title: Federated Action Recognition on Heterogeneous Embedded Devices
• Authors: Pranjal Jain, Shreyas Goenka, Saurabh Bagchi, Biplab Banerjee, Somali Chaterji
• Abstract summary: In this work, we enable clients with limited computing power to perform action recognition, a computationally heavy task. We first perform model compression at the central server through knowledge distillation on a large dataset. The fine-tuning is required because limited data present in smaller datasets is not adequate for action recognition models to learn complextemporal features.
• Score: 16.88104153104136
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Federated learning allows a large number of devices to jointly learn a model without sharing data. In this work, we enable clients with limited computing power to perform action recognition, a computationally heavy task. We first perform model compression at the central server through knowledge distillation on a large dataset. This allows the model to learn complex features and serves as an initialization for model fine-tuning. The fine-tuning is required because the limited data present in smaller datasets is not adequate for action recognition models to learn complex spatio-temporal features. Because the clients present are often heterogeneous in their computing resources, we use an asynchronous federated optimization and we further show a convergence bound. We compare our approach to two baseline approaches: fine-tuning at the central server (no clients) and fine-tuning using (heterogeneous) clients using synchronous federated averaging. We empirically show on a testbed of heterogeneous embedded devices that we can perform action recognition with comparable accuracy to the two baselines above, while our asynchronous learning strategy reduces the training time by 40%, relative to synchronous learning.

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