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Active Federated Learning
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Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients. Downloading models and uploading gradients uses the client's bandwidth, so minimizing these transmission costs is important. The data on each client is highly variable, so the benefit of training on different clients may differ dramatically. To exploit this we propose Active Federated Learning, where in each round clients are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client to maximize efficiency. We propose a cheap, simple and intuitive sampling scheme which reduces the number of required training iterations by 20-70% while maintaining the same model accuracy, and which mimics well known resampling techniques under certain conditions.
Forward citations
Cited by 4 Pith papers
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PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
A client selection rule based on L4-norm cosine similarity and a rotation queue improves federated learning accuracy modestly over three baselines on CIFAR-10 and Fashion-MNIST.
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Fairness in Federated Learning: Fairness for Whom?
A critical review of 121 federated learning fairness papers identifies five recurring pitfalls and proposes a harm-centered, lifecycle-based framework.
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FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View
FedABC combines prediction-similarity scoring with loss-based client values and an increasing participation threshold, reporting higher accuracy with fewer clients on CIFAR-10.
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Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions
A variance-reduction-based client selection with coalition clustering yields modest accuracy gains over baselines in heterogeneous federated learning, but its convergence guarantee rests on an assumption that the poli...
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