A Robust Federated Learning Framework for Undependable Devices at Scale Article Swipe
Shilong Wang
,
Jianchun Liu
,
Hongli Xu
,
Chunming Qiao
,
Huarong Deng
,
Qingbing Zheng
,
Jun Gong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.19991
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.19991
In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks always assume a dependable environment and exclude undependable devices from training, leading to poor model performance and resource wastage. In this paper, we propose FLUDE to effectively deal with undependable environments. First, FLUDE assesses the dependability of devices based on the probability distribution of their historical behaviors (e.g., the likelihood of successfully completing training). Based on this assessment, FLUDE adaptively selects devices with high dependability for training. To mitigate resource wastage during the training phase, FLUDE maintains a model cache on each device, aiming to preserve the latest training state for later use in case local training on an undependable device is interrupted. Moreover, FLUDE proposes a staleness-aware strategy to judiciously distribute the global model to a subset of devices, thus significantly reducing resource wastage while maintaining model performance. We have implemented FLUDE on two physical platforms with 120 smartphones and NVIDIA Jetson devices. Extensive experimental results demonstrate that FLUDE can effectively improve model performance and resource efficiency of FL training in undependable environments.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.19991
- https://arxiv.org/pdf/2412.19991
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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- DOI
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https://doi.org/10.48550/arxiv.2412.19991Digital Object Identifier
- Title
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A Robust Federated Learning Framework for Undependable Devices at ScaleWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-28Full publication date if available
- Authors
-
Shilong Wang, Jianchun Liu, Hongli Xu, Chunming Qiao, Huarong Deng, Qingbing Zheng, Jun GongList of authors in order
- Landing page
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https://arxiv.org/abs/2412.19991Publisher landing page
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https://arxiv.org/pdf/2412.19991Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2412.19991Direct OA link when available
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Scale (ratio), Computer science, Knowledge management, Business, Data science, Artificial intelligence, Geography, CartographyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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