Multi-Source to Multi-Target Decentralized Federated Domain Adaptation

Su Wang, Seyyedali Hosseinalipour, Christopher G. Brinton

Research output: Contribution to journalArticlepeer-review

Abstract

Heterogeneity across devices in federated learning (FL) typically refers to statistical (e.g., non-i.i.d. data distributions) and resource (e.g., communication bandwidth) dimensions. In this paper, we focus on another important dimension that has received less attention: varying quantities/distributions of labeled and unlabeled data across devices. In order to leverage all data, we develop a decentralized federated domain adaptation methodology which considers the transfer of ML models from devices with high quality labeled data (called sources) to devices with low quality or unlabeled data (called targets). Our methodology, Source-Target Determination and Link Formation (ST-LF), optimizes both (i) classification of devices into sources and targets and (ii) source-target link formation, in a manner that considers the trade-off between ML model accuracy and communication energy efficiency. To obtain a concrete objective function, we derive a measurable generalization error bound that accounts for estimates of source-target hypothesis deviations and divergences between data distributions. The resulting optimization problem is a mixed-integer signomial program, a class of NP-hard problems, for which we develop an algorithm based on successive convex approximations to solve it tractably. Subsequent numerical evaluations of ST-LF demonstrate that it improves classification accuracy and energy efficiency over state-of-the-art baselines.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Cognitive Communications and Networking
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

Keywords

  • Adaptation models
  • Automobiles
  • Data models
  • decentralized federated learning
  • Distributed databases
  • federated domain adaptation
  • Federated learning
  • link formation
  • network optimization
  • Optimization
  • Servers
  • Training

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