Online Federated Learning over decentralized networks
Online Federated Learning refers to the online optimization that is distributed over a decentralized network while still seek for training high-quality models. It allows each node to perform local operation and only contacts with its immediate neighbors, liberating it from the control of the `master...
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sg-ntu-dr.10356-1064452020-11-01T05:00:21Z Online Federated Learning over decentralized networks Zhang, Chi Soh Yeng Chai Interdisciplinary Graduate School (IGS) DRNTU::Engineering::Electrical and electronic engineering Online Federated Learning refers to the online optimization that is distributed over a decentralized network while still seek for training high-quality models. It allows each node to perform local operation and only contacts with its immediate neighbors, liberating it from the control of the `master' node. The computation and communication are totally decentralized, avoiding the traffic congestion and network coordination problems that are inevitable to most centralized distributed optimization. Our work addresses several critical problems and their corresponding solutions to make OFL more practical and more efficient in real-scenarios: (a) sampling technique to replace the costly deterministic communication cost with a stochastic strategy; (b) developing and analyzing an algorithm to seek for the optimal saddle point for decentralized online convex-concave problems, and therefore providing solutions for constrained decentralized optimization; (c) avoiding the challenges of “Pareto optimality” when the optimal values for models may only be similar rather than identical. Doctor of Philosophy 2019-03-28T08:26:37Z 2019-12-06T22:11:57Z 2019-03-28T08:26:37Z 2019-12-06T22:11:57Z 2018 Thesis Zhang, C. (2018).Online Federated Learning over decentralized networks. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/106445 http://hdl.handle.net/10220/47928 10.32657/10220/47928 en 123 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Zhang, Chi Online Federated Learning over decentralized networks |
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Online Federated Learning refers to the online optimization that is distributed over a decentralized network while still seek for training high-quality models. It allows each node to perform local operation and only contacts with its immediate neighbors, liberating it from the control of the `master' node. The computation and communication are totally decentralized, avoiding the traffic congestion and network coordination problems that are inevitable to most centralized distributed optimization. Our work addresses several critical problems and their corresponding solutions to make OFL more practical and more efficient in real-scenarios: (a) sampling technique to replace the costly deterministic communication cost with a stochastic strategy; (b) developing and analyzing an algorithm to seek for the optimal saddle point for decentralized online convex-concave problems, and therefore providing solutions for constrained decentralized optimization; (c) avoiding the challenges of “Pareto optimality” when the optimal values for models may only be similar rather than identical. |
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Soh Yeng Chai |
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Soh Yeng Chai Zhang, Chi |
format |
Theses and Dissertations |
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Zhang, Chi |
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Zhang, Chi |
title |
Online Federated Learning over decentralized networks |
title_short |
Online Federated Learning over decentralized networks |
title_full |
Online Federated Learning over decentralized networks |
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Online Federated Learning over decentralized networks |
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Online Federated Learning over decentralized networks |
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online federated learning over decentralized networks |
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2019 |
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https://hdl.handle.net/10356/106445 http://hdl.handle.net/10220/47928 |
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