Papers › Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in...
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing
Xuwei Yang, Anastasis Kratsios, Florian Krach, Matheus Grasselli, Aurelien Lucchi
We propose an optimal iterative scheme for federated transfer learning, where a central planner has access to datasets D₁,…,D_N for the same learning model f_θ. Our objective is to minimize the cumulative deviation of the generated parameters {θᵢ(t)}ₜ₌₀ᵀ across all T iterations from the specialized parameters θ^⋆₁,…,θ^⋆_N obtained for each dataset, while respecting the loss function for the model f_(θ(T)) produced by the algorithm upon halting. We only allow for continual communication between each of the specialized models (nodes/agents) and the central planner (server), at each iteration (round). For the case where the model f_θ is a finite-rank kernel regression, we derive explicit updates for the regret-optimal algorithm. By leveraging symmetries within the regret-optimal algorithm, we further develop a nearly regret-optimal heuristic that runs with 𝒪(Np²) fewer elementary operations, where p is the dimension of the parameter space. Additionally, we investigate the adversarial robustness of the regret-optimal algorithm showing that an adversary which perturbs q training pairs by at-most ε>0, across all training sets, cannot reduce the regret-optimal algorithm's regret by more than 𝒪(εq N̅^(1/2)), where N̅ is the aggregate number of training pairs. To validate our theoretical findings, we conduct numerical experiments in the context of American option pricing, utilizing a randomly generated finite-rank kernel.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections