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A Greedy Agglomerative Framework for Clustered Federated Learning
Manan Mehta,
Chenhui Shao
Mechanical Science and Engineering
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peer-review
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Keyphrases
Greedy
100%
Agglomerative
100%
Federated Learning
100%
Clustered Federated Learning
100%
Client Clustering
45%
Learning Model
18%
Learning Methods
9%
Number of Clusters
9%
Healthcare
9%
Agglomerates
9%
Clustering Results
9%
Underlying Distribution
9%
Vanilla
9%
Autonomous Robots
9%
Preserving Privacy
9%
Client Data
9%
Statistical Heterogeneity
9%
Deep Learning Model
9%
Client-centered
9%
Gradient Update
9%
Multi-source
9%
Clustering Structure
9%
Classification Data
9%
Autonomous Driving
9%
Fault Classification
9%
Federated States
9%
Smart Manufacturing
9%
Non-IID
9%
Hyperparameter Tuning
9%
Mixed Fault
9%
Federated Learning System
9%
Personalized Federated Learning
9%
Decentralized Training
9%
Industrial Big Data
9%
Computer Science
Federated Learning
100%
Case Study
7%
And-States
7%
Privacy Preserving
7%
Big Data
7%
Learning Framework
7%
Identify Cluster
7%
Underlying Distribution
7%
Data Classification
7%
Clustering Result
7%
Data-Client
7%
Deep Learning Model
7%
Clustering Structure
7%
Key Application
7%
Autonomous Driving
7%