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L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning., , , , , and . ICCV, page 16418-16427. IEEE, (2023)High-throughput Simulation of Federated Learning via Resource-Aware Client Placement., , , , , , , and . CoRR, (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 . CoRR, (2022)Feature extraction using MPEG-CDVS and Deep Learning with application to robotic navigation and image classification.. Polytechnic University of Turin, Italy, (2017)base-search.net (ftpoltorinoiris:oai:iris.polito.it:11583/2665943).Protea: Client Profiling within Federated Systems using Flower., , , , and . CoRR, (2022)