Optimized IoT clustering and assignment in semi-synchronous federated learning
Hadi Farajvand, Nahideh Derakhshanfard, Abbas Mirzaei, Ali Ghaffari, Ali Asghar Pour Haji Kazem · 2026
Read Optimized IoT clustering and assignment in semi-synchronous federated learning by Hadi Farajvand, Nahideh Derakhshanfard, Abbas Mirzaei, Ali Ghaffari, Ali Asghar Pour Haji Kazem (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13396435.txt.
Description
Hadi Farajvand and co-authors address a scheduling problem inside federated learning, where many small devices train a shared model without sending their raw data away. Their 2026 study works in a layered setup: internet-of-things devices report to edge servers, and training proceeds in a semi-synchronous rhythm that tolerates slow members.
The authors optimize two choices at once, which devices cluster together and which edge server each cluster reports to, alongside shifting data so clusters stay balanced. Poor clustering wastes battery and bandwidth and drags accuracy down. Their approach aims at higher model quality per unit of resource, an issue that grows as edge networks scale.
Catalog record
| CREATOR | Hadi Farajvand, Nahideh Derakhshanfard, Abbas Mirzaei, Ali Ghaffari, Ali Asghar Pour Haji Kazem |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 121744 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |