2022
DOI: 10.1109/tvcg.2021.3074010
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Inspecting the Running Process of Horizontal Federated Learning via Visual Analytics

Abstract: As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and efficiency of HFL over local training, clues for inspecting the behaviors of the participating clients and the federated model are usually lacking due to the privacy-preserving nature of HFL. Consequently, the users can only conduct a shallow-level analysis of pote… Show more

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Cited by 17 publications
(10 citation statements)
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“…FATEBoard shows basic information like running time, log outputs, and current status for different components while also giving an overview of the progress of jobs [13]. Unfortunately, no detailed information regarding client anomalies is provided [14]. Another alternative is the open-source framework NVIDIA FLARE, which adapts existing workflows for federation and provides visualizations during FL experiments using TensorBoard [15].…”
Section: Federated Learning Visualizationsmentioning
confidence: 99%
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“…FATEBoard shows basic information like running time, log outputs, and current status for different components while also giving an overview of the progress of jobs [13]. Unfortunately, no detailed information regarding client anomalies is provided [14]. Another alternative is the open-source framework NVIDIA FLARE, which adapts existing workflows for federation and provides visualizations during FL experiments using TensorBoard [15].…”
Section: Federated Learning Visualizationsmentioning
confidence: 99%
“…Using this system, users could see how much each client contributed and how the global model improved over time. One of the most comprehensive studies for visualizing FL was conducted by Liet al [14], which proposed a system for inspecting the running process in horizontal FL. In addition to basic visualization of metrics, they enable further client anomaly detection, pairwise comparison of clients, and contribution analysis.…”
Section: Federated Learning Visualizationsmentioning
confidence: 99%
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