Summary of Fedgcs: a Generative Framework For Efficient Client Selection in Federated Learning Via Gradient-based Optimization, by Zhiyuan Ning et al.
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
by Zhiyuan Ning, Chunlin Tian, Meng Xiao, Wei Fan, Pengyang Wang, Li Li, Pengfei Wang, Yuanchun Zhou
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This Federated Learning framework, called FedGCS, addresses the challenges of statistical and system heterogeneity, high energy consumption, and inefficient client selection strategies. By recasting client selection as a generative task, FedGCS efficiently encodes decision-making knowledge within a continuous representation space, enabling gradient-based optimization to search for optimal clients. The framework consists of four steps: collecting diverse “selection-score” pair data, training an encoder-evaluator-decoder model, optimizing in the continuous space, and generating the final client selection via beam search. FedGCS outperforms traditional methods by being more comprehensive, generalizable, and efficient, simultaneously optimizing for model performance, latency, and energy consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way to train AI models using data from many different devices or locations. But it can be tricky because the devices may have different kinds of information and they might not all be connected in the same way. This makes it hard to pick which devices should be used for training. A new approach called FedGCS tries to solve this problem by thinking of client selection as a kind of creative task, like writing a story or generating an image. This allows the system to find the best combination of devices quickly and efficiently, while also taking into account things like how much energy it uses and how fast it can train the model. |
Keywords
» Artificial intelligence » Decoder » Encoder » Federated learning » Optimization
