Federated Domain Adaptation via Gradient Projection
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Federated Domain Adaptation (FDA) describes the federated learning setting where a set of source clients work collaboratively to improve the performance of a target client and where the target client has limited labeled data. The domain shift between the source and target domains, combined with limited samples in the target domain, makes FDA a challenging problem, e.g., common techniques such as FedAvg and fine-tuning fail with a large domain shift. To fill this gap, we propose Federated Gradient Projection (), a novel aggregation rule for FDA, used to aggregate the source gradients and target gradient during training. Further, we introduce metrics that characterize the FDA setting and propose a theoretical framework for analyzing the performance of aggregation rules, which may be of independent interest. Using this framework, we theoretically characterize how, when, and why works compared to baselines. Our theory suggests certain practical rules that are predictive of practice. Experiments on synthetic and real-world datasets verify the theoretical insights and illustrate the effectiveness of the proposed method in practice.
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