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FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler.

, , , , , , , , and . CoRR, (2023)

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FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler., , , , , , , , and . ICLR, OpenReview.net, (2024)Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx., , , , , , , , , and 1 other author(s). CoRR, (2023)Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale., , , , and . Frontiers Artif. Intell., (2022)FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy., , , , , , , , , and . CoRR, (2022)Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources - A Case Study on Federated Fine-tuning of LLaMA 2., , , , , , and . CoRR, (2024)APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service., , , , , , , , , and 2 other author(s). e-Science, page 1-4. IEEE, (2023)FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler., , , , , , , , and . CoRR, (2023)