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High-throughput Simulation of Federated Learning via Resource-Aware Client Placement., , , , , , , and . CoRR, (2023)L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning., , , , , and . ICCV, page 16418-16427. IEEE, (2023)GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks., , , , and . ICRA, page 5474-5480. IEEE, (2019)A first look into the carbon footprint of federated learning., , , , , , , and . CoRR, (2021)Privacy in Multimodal Federated Human Activity Recognition., , , , , , , and . CoRR, (2023)FedVal: Different good or different bad in federated learning., , , , and . USENIX Security Symposium, page 6365-6380. USENIX Association, (2023)Federated Self-supervised Learning for Video Understanding., , , , and . ECCV (31), volume 13691 of Lecture Notes in Computer Science, page 506-522. Springer, (2022)Secure aggregation for federated learning in flower., , , and . DistributedML@CoNEXT, page 8-14. ACM, (2021)Efficient Vertical Federated Learning with Secure Aggregation., , , , , and . CoRR, (2023)SelfVIO: Self-supervised deep monocular Visual-Inertial Odometry and depth estimation., , , , , and . Neural Networks, (2022)