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description: https://www.usenix.org/conference/osdi21/presentation/lai
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# Oort: Efficient Federated Learning via Guided Participant Selection

Trends:&#x20;

* Edge devices: massive data
* Increasing resource on edge devices&#x20;

Federated Learning on the Edge&#x20;

* On-device ML helps&#x20;

Execution of FL&#x20;

* Objective:&#x20;
  * Better time to accuracy&#x20;
* Step
  * Client selection&#x20;
  * Execution&#x20;
  * Aggregation&#x20;

Challenge&#x20;

* FL: heterogeneous&#x20;
  * data distribution&#x20;
* Existing work&#x20;
  * System efficiency
    * Round duration &#x20;
  * Statistical efficiency&#x20;
    * number of rounds to accuracy&#x20;
    * Determined by the client data&#x20;

Problem&#x20;

1. Overlook heter, client utility --> suboptimal training convergence&#x20;
2. Unable for selection criteria --> useless testing results&#x20;

Design

1. Enable faster FL training&#x20;
   1. Challenge: identify heterogenous client utility, select high-utility clients at scale, select high-utility clients adaptively&#x20;
2. Support interpretable FL testing &#x20;

RL:

1. Action space / reward space: dynamics of the action space (might drop)
2. Training overhead&#x20;



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