Personalized Heterogeneity-aware Federated Search Towards Better Accuracy and Energy Efficiency

Federated learning (FL), a new distributed technology, allows us to train the global model on the edge and embedded devices without local data sharing. However, due to the wide distribution of different types of devices, FL faces severe heterogeneity issues. The accuracy and efficiency of FL deploym...

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Vydáno v:2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD) s. 1 - 9
Hlavní autoři: Yang, Zhao, Sun, Qingshuang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 29.10.2022
Témata:
Adaptation models
Computational modeling
Data models
Energy consumption
energy efficiency
Federated learning
federated search
heterogeneity-aware personalization
Performance evaluation
Process control
Training
ISSN:1558-2434
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Shrnutí:Federated learning (FL), a new distributed technology, allows us to train the global model on the edge and embedded devices without local data sharing. However, due to the wide distribution of different types of devices, FL faces severe heterogeneity issues. The accuracy and efficiency of FL deployment at the edge are severely impacted by heterogeneous data and heterogeneous systems. In this paper, we perform joint FL model personalization for heterogeneous systems and heterogeneous data to address the challenges posed by heterogeneities. We begin by using model inference efficiency as a starting point to personalize network scale on each node. Furthermore, it can be used to guide the efficient FL training process, which can help to ease the problem of straggler devices and improve FL's energy efficiency. During FL training, federated search is then used to acquire highly accurate personalized network structures. By taking into account the unique characteristics of FL deployment at edge devices, the personalized network structures obtained by our federated search framework with a lightweight search controller can achieve competitive accuracy with state-of-the-art (SOTA) methods, while reducing inference and training energy consumption by up to 3.57× and 1.82×, respectively.
ISSN:1558-2434
DOI:10.1145/3508352.3549403