Client Selection in Federated Learning: Convergence Analysis and Power-of-ChoiceSelection Strategies

Federated Learning: Why choosing the right clients speeds up training

Federated learning lets many devices train a shared model without sharing private data, but who we pick to talk to the server matters.
New research finds that favoring devices with bigger errors — instead of picking them at random — often makes the whole system learn much faster, so the model gets better sooner.
The paper introduces a simple way called Power-of-Choice that nudges selection toward helpful devices while keeping costs low, and it can be tuned to balance speed and fairness.
Experiments show this idea can converge up to three times quicker and reach higher accuracy than plain random picks, which surprised some people.
The trick is not magic, it's about biasing choices toward where the model still struggles, so updates carry more value.
This approach keeps communication and compute small, so it works for resource-limited devices where saving time and battery matters.
If you're curious about federated learning or managing many edge devices, this shows a practical way to get better models faster, with a small tradeoff you can control.

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Client Selection in Federated Learning: Convergence Analysis and Power-of-ChoiceSelection Strategies

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