> For the complete documentation index, see [llms.txt](https://sliu583.gitbook.io/blog/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sliu583.gitbook.io/blog/conference/index/osdi-21/optimizations-and-scheduling-for-ml/oort-efficient-federated-learning-via-guided-participant-selection.md).

# Oort: Efficient Federated Learning via Guided Participant Selection

https\://www\.usenix.org/conference/osdi21/presentation/lai

Trends: 

* Edge devices: massive data
* Increasing resource on edge devices 

Federated Learning on the Edge 

* On-device ML helps 

Execution of FL 

* Objective: 
  * Better time to accuracy 
* Step
  * Client selection 
  * Execution 
  * Aggregation 

Challenge 

* FL: heterogeneous 
  * data distribution 
* Existing work 
  * System efficiency
    * Round duration  
  * Statistical efficiency 
    * number of rounds to accuracy 
    * Determined by the client data 

Problem 

1. Overlook heter, client utility --> suboptimal training convergence 
2. Unable for selection criteria --> useless testing results 

Design

1. Enable faster FL training 
   1. Challenge: identify heterogenous client utility, select high-utility clients at scale, select high-utility clients adaptively 
2. Support interpretable FL testing  

RL:

1. Action space / reward space: dynamics of the action space (might drop)
2. Training overhead