Amazing technological breakthrough possible @S-Logix

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • +91- 81240 01111

Social List

Unsupervised Federated Learning for Unbalanced Data - 2020

Unsupervised Federated Learning For Unbalanced Data

Research Area:  Machine Learning


This work considers unsupervised learning tasks being implemented within the federated learning framework to satisfy stringent requirements for low-latency and privacy of the emerging applications. The proposed algorithm is based on Dual Averaging (DA), where the gradients of each agent are aggregated at a central node. While having its advantages in terms of distributed computation, the accuracy of federated learning training reduces significantly when the data is nonuniformly distributed across devices. Therefore, this work proposes two weight computation algorithms, with one using a fixed size bin and the other with self-organizing maps (SOM) that solves the underlying dimensionality problem inherent in the first method. Simulation results are also provided to show that the proposed algorithms performance is comparable to the scenario in which all data is uploaded and processed in the centralized cloud.


Author(s) Name:   Mykola Servetnyk; Carrson C. Fung; Zhu Han

Journal name:  

Conferrence name:  GLOBECOM 2020 - 2020 IEEE Global Communications Conference

Publisher name:  IEEE

DOI:  10.1109/GLOBECOM42002.2020.9348203

Volume Information: