Summary of Federated Learning with Dynamic Client Arrival and Departure: Convergence and Rapid Adaptation Via Initial Model Construction, by Zhan-lun Chang et al.
Federated Learning with Dynamic Client Arrival and Departure: Convergence and Rapid Adaptation via Initial Model Construction
by Zhan-Lun Chang, Dong-Jun Han, Rohit Parasnis, Seyyedali Hosseinalipour, Christopher G. Brinton
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to federated learning (FL) that addresses the challenges of dynamic client joins and leaves. In traditional FL, the objective function remains fixed, but this is not the case when clients can dynamically join or leave the system. The authors propose a dynamic optimization objective in FL that seeks an optimal model tailored to the currently active set of clients. They establish an upper bound on the optimality gap, accounting for various factors such as stochastic gradient noise and non-IIDness of data distribution. To enhance adaptability, they also propose an adaptive initial model construction strategy using weighted averaging guided by gradient similarity. The approach is validated on various datasets and FL algorithms, demonstrating robust performance across diverse client arrival and departure patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in machine learning called federated learning. Federated learning means that many devices or computers work together to learn something new, but sometimes these devices might stop working with the group or join later. This makes it hard for them all to agree on what they learned. The scientists came up with a way to make sure everyone agrees and it works well even if some devices leave or come back. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Objective function » Optimization
