Federated Learning and its Applications for Security and Communication

The not so long ago, Artificial Intelligence (AI) has revolutionized our life by giving rise to the idea of self-learning in different environments. Amongst its different variants, Federated Learning (FL) is a novel approach that relies on decentralized communication data and its associated training. While reducing the amount of data acquired from users, federated learning derives the benefits of popular machine learning techniques, it brings learning to the edge or directly on-device. FL, frequently referred to as a new dawn in AI, is still in its early stages and is yet to garner widespread acceptance, owing to its (unknown) security and privacy implications. In this paper, we give an illustrative explanation of FL techniques, communication, and applications with privacy as well as security issues. According to our findings, there are fewer privacy-specific dangers linked with FL than security threats. We conclude the paper with the challenges of FL with special emphases on security.

@article{Asif2022,
  title     = {Federated Learning and its Applications for Security and Communication},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {8},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Hafiz M. Asif and Mohamed Abdul Karim and Firdous Kausar},
  doi       = {10.14569/IJACSA.2022.0130838},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130838}
}