Year: 2023
Type: conference-paper
Abstract: We investigate the trade-off between rate, privacy and storage in federated learning (FL) with top$r$sparsification, where the users and the servers in the FL system only share the most significant$r$and$r^{\prime}$fractions, respectively, of updates and parameters in the FL process, to reduce the communication cost. We present schemes that guarantee information theoretic privacy of the values and indices of the sparse updates sent by the users at the expense of a larger storage cost. To this end, we generalize the scheme to reduce the storage cost by allowing a certain amount of information leakage. Thus, we provide the general trade-off between the communication cost, storage cost, and information leakage in private FL with top$r$sparsification, along the lines of two proposed schemes.
Authors: Sajani Vithana, Sennur Ulukus
Institution: University of Maryland, College Park
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Army Research Office
Award: G4850000551
OpenAlex ID: W4387870397