Federated Learning

The number of Internet of Things (IoT) applications, especially latency-sensitive ones, have been significantly increased. So, cloud computing, as one of the main enablers of the IoT that offers centralized services, cannot solely sat- isfy the requirements of IoT applications. Edge/fog computing, as a distributed computing paradigm, processes, and stores IoT data at the edge of the net- work, offering low latency, reduced network traffic, and higher bandwidth. The edge/fog resources are often less powerful compared to cloud, and IoT data is dispersed among many geo-distributed servers. Hence, Federated Learning (FL), which is a machine learning approach that enables multiple distributed servers to collaborate on building models without exchanging the raw data, is well-suited to edge/fog computing environments, where data privacy is of paramount importance. Besides, to manage different FL tasks on edge/fog computing environments, a lightweight resource management framework is required to manage different incoming FL tasks while does not incur significant overhead on the system.

Team Members @ Melbourne qCLOUDS Lab

External Collaborators

Publications

  1. Shinu M. Rajagopal, Supriya M., and Rajkumar Buyya, FedSDM: Federated Learning based Smart Decision Making Module for ECG Data in IoT Integrated Edge-Fog-Cloud Computing Environments, Internet of Things: Engineering Cyber Physical Human Systems, Volume 22, Pages: 1-20, ISSN: 2542-6605, Elsevier Press, Amsterdam, The Netherlands, July 2023.
  2. Wuji Zhu, Mohammad Goudarzi, and Rajkumar Buyya, FLight: A Lightweight Federated Learning Framework in Edge and Fog Computing, Software: Practice and Experience (SPE), Volume 54, Issue 5, Pages: 813-841, ISSN: 0038-0644, Wiley Press, New York, USA, May 2024.
  3. Shinu M. Rajagopal, Supriya M., Rajkumar Buyya, Blockchain Integrated Federated Learning in Edge-Fog-Cloud Systems for IoT based Healthcare Applications: A Survey, Federated Learning: Principles, Paradigms, and Applications, 237-269pp, J. Sahoo, M. Ouaissa, and Akarsh K. Nair (eds), ISBN: 978-177-49-1638-4, Apple Academic Press, Inc., Palm Bay, Florida, USA, September 2024.
  4. Shinu M. Rajagopal, Supriya M., and Rajkumar Buyya, Leveraging Blockchain and Federated Learning in Edge-Fog-Cloud Computing Environments for Intelligent Decision-Making with ECG Data in IoT, Journal of Network and Computer Applications (JNCA), Volume 233, Pages: 1-16, ISSN: 1084-8045, Elsevier, Amsterdam, The Netherlands, January 2025.
  5. Anwesha Mukherjee and Rajkumar Buyya, Federated Learning Architectures: A Performance Evaluation With Crop Yield Prediction Application, Software: Practice and Experience (SPE), Volume 55, Issue 7, Pages: 1165-1184, ISSN: 0038-0644, Wiley Press, New York, USA, July 2025.
  6. Tanushree Dey, Somnath Bera, Anwesha Mukherjee, Debashis De, and Rajkumar Buyya, FLyer: Federated Learning-based Crop Yield Prediction for Agriculture 5.0, IEEE Transactions on Artificial Intelligence (TAI), Volume 6, No. 7, Pages: 1943-1952, ISSN: 1063-6706, IEEE Press, New York, USA, July 2025.
  7. Anwesha Mukherjee and Rajkumar Buyya, A Joint Time and Energy-Efficient Federated Learning-based Computation Offloading Method for Mobile Edge Computing, Future Generation Computer Systems (FGCS), Volume 178, Pages: 1-15, ISSN: 0167-739X, Elsevier Press, Amsterdam, The Netherlands, May 2026.
  8. Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning

Software Systems (Open Source)

       
The Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory
School of Computing and Information Systems
The University of Melbourne, Australia