fl-client-selection

a very brief summary of client selections in federated learnings

[TOC]

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

IEEE TWC 2019

Motivation

existing works focus on the problem in individual learning ⇒ overlooking long term performance

Client Selection

Goal & Criterion:

• energy efficient

  • transmission power (Shannon's formula)
  • local training energy consumption

• later training result is better (exp. verification, ascend > uniform || descend) in terms of loss, accuracy, robust

  • remark: mostly empirical, may need more experiments on temporal pattern

• more client is better [2] [3]

Solve:

incorporated client selection into overall performance optimization using Lyapunov method

use decision variable to decide whether a client is selected

FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning

IEEE IOTJ 2021

Motivation:

current complete/quasi random client selection cannot handle the heterogeneity of the client devices ⇒ some of the client may fail to complete task ⇒ many discarded learning rounds ⇒ affect the model accuracy

Client Selection (FedMCCS)

Goal:

• consider the heterogeneity of the clients
• their limited computation and communication resources
• achieve maximum number of those that can complete FL rounds with out system cash
• maintain high-performance Fl mode in the mean time

Criterion:

• resource utilization is under budget per device type
  • CPU frequency
  • memory cost
  • energy cost
• time needed to complete a round is under threshold
  • download time
  • predicted update time (RUPred-LR)
  • upload time
• maximize event rate (?)

Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning

IEEE IOTJ 2021

Motivation:

optimize accuracy in stateful FL with a budgeted number of candidate clients

improve test accuracy

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Client Selection:

Goal: select the $R$ best candidate clients based on their test accuracy

Criterion: test accuracy

Formulation & Solve: similar to secretary problem [4] [5]

• evaluate the test accuracy of the first $\alpha^*$ clients and reject them all
• select the next $R$ clients with test accuracy better than the best test accuracy of the first $\alpha^*$ clients
• if none is found then select the last clients

How to select the value of $\alpha^*$ is a problem of probability.

An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee

IEEE TPDS 2021

Motivation:

1. the number of clients could be sufficiently large & bandwidth is limited ⇒ should select clients
2. clients with low priority are simply being deprived of chances to participate at the same time ⇒ data imbalanced

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Client Selection (C2MAB + RBCS-F)

Goal: select clients to minimize long-term model exchange time while guaranteeing fairness

Criterion:

• long-term fairness

• availability of clients

• selection fraction: sometimes available clients is not enough

Solve:

Lyapunov method

and one more thing to answer: how to calculate the model exchange time?

here using Contextual Combinatorial Multi Arm Bandit

The methods and formulations in this paper is highly similar to TWC2020

SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead

IEEE TC 2021

Motivation

• address problems in federated learning in extreme conditions
• mitigate the impacts of stragglers, crashes and model staleness

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Age-Based Scheduling Policy for Federated Learning in Mobile Edge Network

IEEE ICASSP 2020

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Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Network Systems

IEEE Communications Survey & Tutorials 2021