Multi-Stage Hybrid Federated Learning Over Large-Scale D2D-Enabled Fog Networks

Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton, Nicolo Michelusi, Vaneet Aggarwal, David J. Love, Huaiyu Dai

Research output: Contribution to journalArticlepeer-review

Abstract

Federated learning has generated significant interest, with nearly all works focused on a ``star'' topology where nodes/devices are each connected to a central server. We migrate away from this architecture and extend it through the network dimension to the case where there are multiple layers of nodes between the end devices and the server. Specifically, we develop multi-stage hybrid federated learning (MH-FL), a hybrid of intra- and inter-layer model learning that considers the network as a multi-layer cluster-based structure. MH-FL considers the topology structures among the nodes in the clusters, including local networks formed via device-to-device (D2D) communications, and presumes a semi-decentralized architecture for federated learning. It orchestrates the devices at different network layers in a collaborative/cooperative manner (i.e., using D2D interactions) to form local consensus on the model parameters and combines it with multi-stage parameter relaying between layers of the tree-shaped hierarchy. We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e.g., the spectral radius) and the learning algorithm (e.g., the number of D2D rounds in different clusters). We obtain a set of policies for the D2D rounds at different clusters to guarantee either a finite optimality gap or convergence to the global optimum. We then develop a distributed control algorithm for MH-FL to tune the D2D rounds in each cluster over time to meet specific convergence criteria. Our experiments on real-world datasets verify our analytical results and demonstrate the advantages of MH-FL in terms of resource utilization metrics.

Original languageEnglish (US)
JournalIEEE/ACM Transactions on Networking
DOIs
StateAccepted/In press - 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Collaborative work
  • Computational modeling
  • Convergence
  • cooperative learning
  • Device-to-device communication
  • device-to-device communications
  • distributed machine learning
  • Fog learning
  • peer-to-peer learning
  • semi-decentralized federated learning.
  • Servers
  • Topology
  • Training

Fingerprint

Dive into the research topics of 'Multi-Stage Hybrid Federated Learning Over Large-Scale D2D-Enabled Fog Networks'. Together they form a unique fingerprint.

Cite this