Pollen: High-throughput Federated Learning Simulation via Resource-Aware Client Placement Article Swipe
Lorenzo Sani
,
Pedro Porto Buarque de Gusmão
,
Alex Iacob
,
Wanru Zhao
,
Xinchi Qiu
,
Yan Gao
,
Javier Fernández-Marqués
,
Nicholas D. Lane
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.17453
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.17453
Federated Learning (FL) is a privacy-focused machine learning paradigm that collaboratively trains models directly on edge devices. Simulation plays an essential role in FL adoption, helping develop novel aggregation and client sampling strategies. However, current simulators cannot emulate large-scale systems in a time-efficient manner, which limits their utility and casts doubts on generalizability. This work proposes Pollen, a novel resource-aware system for speeding up simulations. Pollen addresses two limiting factors from existing simulators: (a) communication inefficiency derived from pull-based client execution and (b) inadequate load balance when using heterogeneous hardware. Pollen executes high-throughput FL simulations at scale by (a) using a push-based client placement system, (b) learning how an adaptable scheduling of clients based on hardware statistics (c) estimating the optimal number of concurrent workers per GPU. We evaluate Pollen on four representative FL tasks and show that Pollen's placement model increases GPU utilization and reduces idle time. We compare Pollen to Flower, Flute, FedScale, Parrot, and pfl and show experimental speed-ups of days or weeks.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.17453
- https://arxiv.org/pdf/2306.17453
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4383046641Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.17453Digital Object Identifier
- Title
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Pollen: High-throughput Federated Learning Simulation via Resource-Aware Client PlacementWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-30Full publication date if available
- Authors
-
Lorenzo Sani, Pedro Porto Buarque de Gusmão, Alex Iacob, Wanru Zhao, Xinchi Qiu, Yan Gao, Javier Fernández-Marqués, Nicholas D. LaneList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.17453Publisher landing page
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https://arxiv.org/pdf/2306.17453Direct 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
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https://arxiv.org/pdf/2306.17453Direct OA link when available
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Computer science, Throughput, Server, Distributed computing, Scale (ratio), Limiting, Theoretical computer science, Computer network, Operating system, Mechanical engineering, Physics, Engineering, Quantum mechanics, WirelessTop 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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| abstract_inverted_index.hardware. | 89 |
| abstract_inverted_index.increases | 141 |
| abstract_inverted_index.placement | 103, 139 |
| abstract_inverted_index.speed-ups | 161 |
| abstract_inverted_index.Simulation | 17 |
| abstract_inverted_index.concurrent | 123 |
| abstract_inverted_index.estimating | 118 |
| abstract_inverted_index.inadequate | 83 |
| abstract_inverted_index.pull-based | 78 |
| abstract_inverted_index.push-based | 101 |
| abstract_inverted_index.scheduling | 110 |
| abstract_inverted_index.simulators | 35 |
| abstract_inverted_index.statistics | 116 |
| abstract_inverted_index.aggregation | 28 |
| abstract_inverted_index.large-scale | 38 |
| abstract_inverted_index.simulations | 94 |
| abstract_inverted_index.simulators: | 72 |
| abstract_inverted_index.strategies. | 32 |
| abstract_inverted_index.utilization | 143 |
| abstract_inverted_index.experimental | 160 |
| abstract_inverted_index.inefficiency | 75 |
| abstract_inverted_index.simulations. | 64 |
| abstract_inverted_index.communication | 74 |
| abstract_inverted_index.heterogeneous | 88 |
| abstract_inverted_index.representative | 132 |
| abstract_inverted_index.resource-aware | 59 |
| abstract_inverted_index.time-efficient | 42 |
| abstract_inverted_index.collaboratively | 10 |
| abstract_inverted_index.high-throughput | 92 |
| abstract_inverted_index.privacy-focused | 5 |
| abstract_inverted_index.generalizability. | 52 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |