Sep 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 63 references
Privacy-Preserving Technologies in Data
TL;DR
Reliability metadata improve federated aggregation when they predict the value of a client update for other participants when they predict the value of a client update for other participants using auditable cross-client update-utility certificates.
Federated learning reduces raw-data movement, but partial participation under statistical and system heterogeneity makes the selected client cohort a major source of optimisation bias and delay. This study proposes a history-aware multi-objective selector that combines opt-in data-quality metadata, exponentially smooth...
Hao-Xuan Geng· Applied and Computational En...· 0 citations
Federated learning (FL) can train a shared model without pooling client records, but it does not by itself provide differential privacy (DP). We compare centralized training, local-only training, federated averaging (FedAvg), and client-level DP-FedAvg on a fully released synthetic benchmark of free-text root-cause-ana...
Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space using an NTK-based agreement score to characterize predictive behavior and determine a persona...
Mirko Konstantin, S. Zachow, Anirban Mukhopadhyay· 0 citations
Personalized federated learning combines shared representations with client-specific predictors, but the contribution of a server weighting rule can be obscured by local training and evaluation choices. We study ORDERS, a configuration that combines a shared backbone, a private residual adapter and classifier, geometri...
The results suggest that compositional shared adaptation with local residual personalization is a practical direction for financial LLM deployment under data-local, communication-constrained, and heterogeneous federated settings.
Bo-Wen Li, Si-Yuan Ma, Yang Liu· Electronics· 0 citations
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