Oort: Efficient Federated Learning via Guided Participant Selection



  • 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


  • FL: heterogeneous

    • data distribution

  • Existing work

    • System efficiency

      • Round duration

    • Statistical efficiency

      • number of rounds to accuracy

      • Determined by the client data


  1. Overlook heter, client utility --> suboptimal training convergence

  2. Unable for selection criteria --> useless testing results


  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


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

  2. Training overhead

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