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FabricCRDT: A Conflict-Free Replicated Datatypes Approach to Permissioned Blockchains., , and . Middleware, page 110-122. ACM, (2019)An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training., , , and . CoRR, (2023)Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly., , , , and . CoRR, (2023)FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems., , , and . CoRR, (2023)The Evolution of Distributed Systems for Graph Neural Networks and Their Origin in Graph Processing and Deep Learning: A Survey., , and . ACM Comput. Surv., 56 (1): 6:1-6:37 (January 2024)Choosing a Classical Planner with Graph Neural Networks., , , , and . CoRR, (2024)How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study., , and . CoRR, (2023)FabricCRDT: A Conflict-Free Replicated Datatypes Approach to Permissioned Blockchains., , and . CoRR, (2023)Federated office plug-load identification for building management systems., , , and . e-Energy, page 114-126. ACM, (2022)OrderlessChain: a CRDT-enabled blockchain without total global order of transactions: poster abstract., , and . Middleware Demos/Posters, page 5-6. ACM, (2022)