Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks Article Swipe
Aleksei Ponomarenko-Timofeev
,
Olga Galinina
,
Ravikumar Balakrishnan
,
Nageen Himayat
,
Sergey Andreev
,
Yevgeni Koucheryavy
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.10254
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.10254
Federated systems enable collaborative training on highly heterogeneous data through model personalization, which can be facilitated by employing multi-task learning algorithms. However, significant variation in device computing capabilities may result in substantial degradation in the convergence rate of training. To accelerate the learning procedure for diverse participants in a multi-task federated setting, more efficient and robust methods need to be developed. In this paper, we design an efficient iterative distributed method based on the alternating direction method of multipliers (ADMM) for support vector machines (SVMs), which tackles federated classification and regression. The proposed method utilizes efficient computations and model exchange in a network of heterogeneous nodes and allows personalization of the learning model in the presence of non-i.i.d. data. To further enhance privacy, we introduce a random mask procedure that helps avoid data inversion. Finally, we analyze the impact of the proposed privacy mechanisms and participant hardware and data heterogeneity on the system performance.
Related Topics
Concepts
Computer science
Personalization
Support vector machine
Task (project management)
Machine learning
Rate of convergence
Federated learning
Computation
Distributed computing
Artificial intelligence
Convergence (economics)
Data mining
Computer network
Algorithm
Economic growth
Economics
World Wide Web
Channel (broadcasting)
Management
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.10254
- https://arxiv.org/pdf/2303.10254
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4330336730
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4330336730Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2303.10254Digital Object Identifier
- Title
-
Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-17Full publication date if available
- Authors
-
Aleksei Ponomarenko-Timofeev, Olga Galinina, Ravikumar Balakrishnan, Nageen Himayat, Sergey Andreev, Yevgeni KoucheryavyList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.10254Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2303.10254Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2303.10254Direct OA link when available
- Concepts
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Computer science, Personalization, Support vector machine, Task (project management), Machine learning, Rate of convergence, Federated learning, Computation, Distributed computing, Artificial intelligence, Convergence (economics), Data mining, Computer network, Algorithm, Economic growth, Economics, World Wide Web, Channel (broadcasting), ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.tackles | 86 |
| abstract_inverted_index.through | 9 |
| abstract_inverted_index.Finally, | 134 |
| abstract_inverted_index.However, | 21 |
| abstract_inverted_index.exchange | 99 |
| abstract_inverted_index.hardware | 146 |
| abstract_inverted_index.learning | 19, 42, 111 |
| abstract_inverted_index.machines | 83 |
| abstract_inverted_index.presence | 115 |
| abstract_inverted_index.privacy, | 122 |
| abstract_inverted_index.proposed | 92, 141 |
| abstract_inverted_index.setting, | 51 |
| abstract_inverted_index.training | 4 |
| abstract_inverted_index.utilizes | 94 |
| abstract_inverted_index.Federated | 0 |
| abstract_inverted_index.computing | 26 |
| abstract_inverted_index.direction | 75 |
| abstract_inverted_index.efficient | 53, 67, 95 |
| abstract_inverted_index.employing | 17 |
| abstract_inverted_index.federated | 50, 87 |
| abstract_inverted_index.introduce | 124 |
| abstract_inverted_index.iterative | 68 |
| abstract_inverted_index.procedure | 43, 128 |
| abstract_inverted_index.training. | 38 |
| abstract_inverted_index.variation | 23 |
| abstract_inverted_index.accelerate | 40 |
| abstract_inverted_index.developed. | 60 |
| abstract_inverted_index.inversion. | 133 |
| abstract_inverted_index.mechanisms | 143 |
| abstract_inverted_index.multi-task | 18, 49 |
| abstract_inverted_index.non-i.i.d. | 117 |
| abstract_inverted_index.algorithms. | 20 |
| abstract_inverted_index.alternating | 74 |
| abstract_inverted_index.convergence | 35 |
| abstract_inverted_index.degradation | 32 |
| abstract_inverted_index.distributed | 69 |
| abstract_inverted_index.facilitated | 15 |
| abstract_inverted_index.multipliers | 78 |
| abstract_inverted_index.participant | 145 |
| abstract_inverted_index.regression. | 90 |
| abstract_inverted_index.significant | 22 |
| abstract_inverted_index.substantial | 31 |
| abstract_inverted_index.capabilities | 27 |
| abstract_inverted_index.computations | 96 |
| abstract_inverted_index.participants | 46 |
| abstract_inverted_index.performance. | 153 |
| abstract_inverted_index.collaborative | 3 |
| abstract_inverted_index.heterogeneity | 149 |
| abstract_inverted_index.heterogeneous | 7, 104 |
| abstract_inverted_index.classification | 88 |
| abstract_inverted_index.personalization | 108 |
| abstract_inverted_index.personalization, | 11 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |