Multi-arm bandit-led clustering in federated learning
Federated Learning (FL) is a machine learning technique that enables the training of models across decentralized devices or nodes, without requiring the raw data to be centrally collected in one location. Instead, the model is trained in a distributed manner across multiple nodes, with each node onl...
Saved in:
Main Author: | Zhao, Joe Chen Xuan |
---|---|
Other Authors: | Anupam Chattopadhyay |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/175424 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Multi-armed linear bandits with latent biases
by: Kang, Qiyu, et al.
Published: (2024) -
Dynamic Clustering of Contextual Multi-Armed Bandits
by: NGUYEN, Trong T., et al.
Published: (2014) -
THE CONFIDENCE BOUND METHOD FOR THE MULTI-ARMED BANDIT PROBLEM WITH LARGE ARM SIZE
by: HU SHOURI
Published: (2020) -
PERFORMANCE GUARANTEES FOR ONLINE LEARNING: CASCADING BANDITS AND ADVERSARIAL CORRUPTIONS
by: ZHONG ZIXIN
Published: (2021) -
Efficient resource allocation with fairness constraints in restless multi-armed bandits
by: LI, Dexun, et al.
Published: (2022)