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DistributedML@CoNEXT 2023: Paris, France

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Stefanos Laskaridis, Alexey Tumanov, Nathalie Baracaldo, Dimitrios Vytiniotis:
Proceedings of the 4th International Workshop on Distributed Machine Learning, DistributedML 2023, Paris, France, 8 December 2023. ACM 2023
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  - conf/distributedml/SingapuramHLZC23
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Sanjay Sri Vallabh Singapuram, Chuheng Hu, Fan Lai, Chengsong Zhang, Mosharaf Chowdhury:
Flamingo: A User-Centric System for Fast and Energy-Efficient DNN Training on Smartphones. 1-10
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  - conf/distributedml/StripelisATAA23
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Dimitris Stripelis, Chrysovalantis Anastasiou, Patrick Toral, Armaghan Asghar, José Luis Ambite:
MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows. 11-19
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Hongrui Shi, Valentin Radu, Po Yang:
Lightweight Workloads in Heterogeneous Federated Learning via Few-shot Learning. 21-26
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  - conf/distributedml/WulfertACWG23
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Lars Wulfert, Navidreza Asadi, Wen-Yu Chung, Christian Wiede, Anton Grabmaier:
Adaptive Decentralized Federated Gossip Learning for Resource-Constrained IoT Devices. 27-33
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Jihao Xin, Ivan Ilin, Shunkang Zhang, Marco Canini, Peter Richtárik:
Kimad: Adaptive Gradient Compression with Bandwidth Awareness. 35-48
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Konstantin Burlachenko, Abdulmajeed Alrowithi, Fahad Ali Albalawi, Peter Richtárik:
Federated Learning is Better with Non-Homomorphic Encryption. 49-84
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  - conf/distributedml/MalinovskyMR23
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Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik:
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. 85-104

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