Inverse Feasibility in Over-the-Air Federated Learning
This work addresses security and privacy issues in federated learning networks by offering a complementary theoretical framework, though it appears incremental as it builds on existing models.
The paper tackles the problem of enhancing over-the-air federated learning algorithms by introducing the concept of inverse feasibility for linear forward models, which provides an upper bound on the condition number as a function of parameters, and proposes a new model with improvements identified through analysis and numerical experiments.
We introduce the concept of inverse feasibility for linear forward models as a tool to enhance OTA FL algorithms. Inverse feasibility is defined as an upper bound on the condition number of the forward operator as a function of its parameters. We analyze an existing OTA FL model using this definition, identify areas for improvement, and propose a new OTA FL model. Numerical experiments illustrate the main implications of the theoretical results. The proposed framework, which is based on inverse problem theory, can potentially complement existing notions of security and privacy by providing additional desirable characteristics to networks.