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Title: MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning
There has recently been an increasing interest in computationally-efficient learning methods for resource-constrained applications, e.g., pruning, quantization and channel gating. In this work, we advocate a holistic approach to jointly train the backbone network and the channel gating which can speed up subnet selection for a new task at the resource-limited node. In particular, we develop a federated meta-learning algorithm to jointly train good meta-initializations for both the backbone networks and gating modules, by leveraging the model similarity across learning tasks on different nodes. In this way, the learnt meta-gating module effectively captures the important filters of a good meta-backbone network, and a task-specific conditional channel gated network can be quickly adapted from the meta-initializations using data samples of the new task. The convergence of the proposed federated meta-learning algorithm is established under mild conditions. Experimental results corroborate the effectiveness of our method in comparison to related work.  more » « less
Award ID(s):
2121222 2203239 2203412
NSF-PAR ID:
10352357
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
Page Range / eLocation ID:
164 to 172
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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