Please note: This master’s thesis presentation will take place in DC2314.
Sreepriya Pulyassary, Master’s candidate
David R. Cheriton School of Computer Science
How should a central server aggregate predictions from multiple agents, each holding
partial or overlapping information, into a single collective decision — without exposing
any individual agent’s internal model or reasoning? Existing Federated Learning methods
which satisfy our low-information requirements do not sufficiently address the online aspect
of our setting. Online Learning supplies exactly this kind of guarantee, but its aggregation
methods assume that some single agent is competitive on its own — an assumption that
fails in the partial-feature setting VFL requires. This thesis aims to close this gap by
proposing methods for principled aggregation under partial feature coverage, framing this
as a social choice problem. We propose a novel server strategy Trust-Based Perpetual
Voting (TBPV) , which aggregates agent ballots through a scoring rule whose weights are
learned online from the history of collective outcomes.