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GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning.

, , , , , and . CoRR, (2022)

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Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions., , , , , , , and . EMNLP (Industry Track), page 29-62. Association for Computational Linguistics, (2022)NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions., , , , , , , , , and 1 other author(s). NeurIPS (Competition and Demos), volume 220 of Proceedings of Machine Learning Research, page 189-203. PMLR, (2021)Accelerating Federated Learning via Sampling Anchor Clients with Large Batches., , , , and . CoRR, (2022)Anchor Sampling for Federated Learning with Partial Client Participation., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 37379-37416. PMLR, (2023)GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning., , , , , and . CoRR, (2022)Sign bit is enough: a learning synchronization framework for multi-hop all-reduce with ultimate compression., , , , , , and . DAC, page 193-198. ACM, (2022)NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions., , , , , , , , , and 1 other author(s). CoRR, (2023)Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning., , , , , and . CoRR, (2024)