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Bandwidth Slicing t...
Bandwidth Slicing to Boost Federated Learning over Passive Optical Networks
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- Li, Jun, 1988 (author)
- Chalmers University of Technology, Sweden,Chalmers tekniska högskola
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- Shen, Xiaoman (author)
- Zhejiang University, China
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- Chen, Lei (author)
- RISE,Mobilitet och system,RISE Research Institutes of Sweden
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- Chen, Jiajia, 1981 (author)
- Chalmers University of Technology, Sweden,Chalmers tekniska högskola
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2020
- 2020
- English.
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In: IEEE Communications Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1089-7798 .- 1558-2558. ; 24:7, s. 1492-1495
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- During federated learning (FL) process, each client needs to periodically upload local model parameters and download global model parameters to/from the central server, thus requires efficient communications. Meanwhile, passive optical network (PON) is promising to support fog computing where FL tasks can be executed and the traffic generated by FL needs to be transmitted together with other types of traffic for broadband access. In this letter, a bandwidth slicing algorithm in PONs is introduced for efficient FL, in which bandwidth is reserved for the involved ONUs collaboratively and mapped into each polling cycle. Results reveal that the proposed bandwidth slicing significantly improves training efficiency while achieving good learning accuracy for the FL task running over the PON.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Bandwidth slicing
- federated learning
- fog computing
- passive optical networks
- Bandwidth
- Passive networks
- Broadband access
- Central servers
- Client needs
- Efficient communications
- Global modeling
- Learning accuracy
- Slicing algorithms
- Training efficiency
Publication and Content Type
- ref (subject category)
- art (subject category)
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