Keywords: Federated Learning, Differential Privacy, Image Classification
TL;DR: This paper describes the FLAIR dataset that we are releasing later this month to accelerate research in Federated Learning. This is a large image dataset that is heterogenous, with images grouped by Flicker users and annotated by human.
Abstract: Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing federated learning benchmarks in the image domain do not accurately capture the scale and heterogeneity of many real-world use cases. We introduce FLAIR, a challenging large-scale annotated image dataset for multi-label classification suitable for federated learning. FLAIR has 429,078 images from 51,414 Flickr users and captures many of the intricacies typically encountered in federated learning, such as heterogeneous user data and a long-tailed label distribution. We implement multiple baselines in different learning setups for different tasks on this dataset. We believe FLAIR can serve as a challenging benchmark for advancing the state-of-the art in federated learning. Dataset access and the code for the benchmark are available at https://github.com/apple/ml-flair.
Supplementary Material: pdf
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Dataset Url: https://github.com/apple/ml-flair
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Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2207.08869/code)