TriFed:Trustworthy Personalized Federated Generalization for Unseen Clients under Open Distribution Shifts

Federated learning is valuable when data cannot be shared, yet most existing methods implicitly assume a relatively closed set of training clients and largely stationary local distributions, and often interpret deviations from the global direction uniformly as personalization signals. In open environments, the arrival of unseen clients, distribution drift, and fluctuations in update quality may occur simultaneously, coupling shared-knowledge transfer, local personalization, and the suppression of unreliable effects. To address this setting, we propose TriFed, a trustworthy personalized federated generalization framework. Based on cross-client evidence and local utility, TriFed organizes training contributions into three functional roles—shared retention, personalized reinforcement, and harmful suppression. Locally useful knowledge can therefore participate in shared learning while strengthening local discrimination, whereas sample contributions supported by negative-utility evidence are filtered through cross-round confirmation. On the server, global aggregation and cluster-level collaboration form two channels with isolated training and deployment states; at deployment, unseen clients use shared-model initialization and lightweight support-set adaptation. We evaluate seen and tail client performance, unseen-client zero-shot and adapted accuracy, open-perturbation responses, and overhead, with structural ablations of tri-decomposition and dual-channel collaboration. Ablation and gating diagnostics indicate distinct roles: tri-decomposition mainly affects robustness under open shifts and contamination, the private head primarily supports seen-client performance, and the measurable benefit of cluster-level collaboration is concentrated in adaptation initialization for unseen clients. On FEMNIST with 20% label noise, TriFed maintains both the average and bottom-10% performance of seen clients, while ranking within the top two entries of the same table for unseen-client zero-shot prediction and adaptation.