Overcoming Noisy and Irrelevant Data in Federated Learning

• URL: http://arxiv.org/abs/2001.08300v2
• Date: Tue, 23 Jun 2020 02:12:29 GMT
• Title: Overcoming Noisy and Irrelevant Data in Federated Learning
• Authors: Tiffany Tuor, Shiqiang Wang, Bong Jun Ko, Changchang Liu, Kin K. Leung
• Abstract summary: Federated learning is an effective way of training a machine learning model in a distributed manner from local data collected by client devices. We propose a method for distributedly selecting relevant data, where we use a benchmark model trained on a small benchmark dataset. The effectiveness of our proposed approach is evaluated on multiple real-world image datasets in a simulated system with a large number of clients.
• Score: 13.963024590508038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and communication bandwidth restrictions. Federated learning is an effective way of training a machine learning model in a distributed manner from local data collected by client devices, which does not require exchanging the raw data among clients. A challenge is that among the large variety of data collected at each client, it is likely that only a subset is relevant for a learning task while the rest of data has a negative impact on model training. Therefore, before starting the learning process, it is important to select the subset of data that is relevant to the given federated learning task. In this paper, we propose a method for distributedly selecting relevant data, where we use a benchmark model trained on a small benchmark dataset that is task-specific, to evaluate the relevance of individual data samples at each client and select the data with sufficiently high relevance. Then, each client only uses the selected subset of its data in the federated learning process. The effectiveness of our proposed approach is evaluated on multiple real-world image datasets in a simulated system with a large number of clients, showing up to $25\%$ improvement in model accuracy compared to training with all data.

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