Peer-to-peer federated learning
This Final Year Project (FYP) explores the integration of Peer-to-Peer Federated Learning (P2P FL) on Android devices, leveraging TensorFlow Lite alongside Wi-Fi Direct and Bluetooth for decentralized machine learning (ML) model training directly on mobile devices. The study aims to harness the col...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/175217 |
| _version_ | 1826114649049792512 |
|---|---|
| author | Sim, Nicholas Yong Yue |
| author2 | Anupam Chattopadhyay |
| author_facet | Anupam Chattopadhyay Sim, Nicholas Yong Yue |
| author_sort | Sim, Nicholas Yong Yue |
| collection | NTU |
| description | This Final Year Project (FYP) explores the integration of Peer-to-Peer Federated Learning (P2P FL) on Android devices, leveraging TensorFlow Lite alongside Wi-Fi Direct and
Bluetooth for decentralized machine learning (ML) model training directly on mobile devices. The study aims to harness the collective computational power of smartphones
to collaboratively improve ML models while ensuring user data privacy. Experiments
conducted across various Android devices, demonstrate the framework’s adaptability to
different hardware and software environments, ensuring efficient model training and synchronization despite device diversity. The project highlights the potential of P2P FL in
making artificial intelligence more accessible and customizable across a broad spectrum
of devices. However, challenges such as communication overhead, scalability, and mobile
device limitations are acknowledged, emphasizing the need for ongoing research and optimization in P2P FL methodologies. This work contributes to the evolving field of FL,
offering insights into its application on mobile platforms and outlining future directions
for leveraging mobile devices in collaborative learning scenarios. |
| first_indexed | 2024-10-01T03:42:34Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175217 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:42:34Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1752172024-04-26T15:42:54Z Peer-to-peer federated learning Sim, Nicholas Yong Yue Anupam Chattopadhyay School of Computer Science and Engineering anupam@ntu.edu.sg Computer and Information Science Federated learning This Final Year Project (FYP) explores the integration of Peer-to-Peer Federated Learning (P2P FL) on Android devices, leveraging TensorFlow Lite alongside Wi-Fi Direct and Bluetooth for decentralized machine learning (ML) model training directly on mobile devices. The study aims to harness the collective computational power of smartphones to collaboratively improve ML models while ensuring user data privacy. Experiments conducted across various Android devices, demonstrate the framework’s adaptability to different hardware and software environments, ensuring efficient model training and synchronization despite device diversity. The project highlights the potential of P2P FL in making artificial intelligence more accessible and customizable across a broad spectrum of devices. However, challenges such as communication overhead, scalability, and mobile device limitations are acknowledged, emphasizing the need for ongoing research and optimization in P2P FL methodologies. This work contributes to the evolving field of FL, offering insights into its application on mobile platforms and outlining future directions for leveraging mobile devices in collaborative learning scenarios. Bachelor's degree 2024-04-21T12:43:20Z 2024-04-21T12:43:20Z 2024 Final Year Project (FYP) Sim, N. Y. Y. (2024). Peer-to-peer federated learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175217 https://hdl.handle.net/10356/175217 en application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Federated learning Sim, Nicholas Yong Yue Peer-to-peer federated learning |
| title | Peer-to-peer federated learning |
| title_full | Peer-to-peer federated learning |
| title_fullStr | Peer-to-peer federated learning |
| title_full_unstemmed | Peer-to-peer federated learning |
| title_short | Peer-to-peer federated learning |
| title_sort | peer to peer federated learning |
| topic | Computer and Information Science Federated learning |
| url | https://hdl.handle.net/10356/175217 |
| work_keys_str_mv | AT simnicholasyongyue peertopeerfederatedlearning |