{"ID":3004654,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-05T11:43:53.432517148Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.03939","arxiv_id":"2606.03939","title":"FlashbackCL: Mitigating Temporal Forgetting in Federated Learning","abstract":"Federated Learning (FL) of foundation and edge models increasingly targets deployments where client data distributions drift over time, yet existing forgetting-mitigation methods assume each client's distribution is stationary. Flashback, the strongest recent FL method against cross-client (spatial) forgetting, uses monotonically accumulating per-class label counts as a knowledge proxy; this proxy becomes miscalibrated under temporal distribution shift and anchors the global model to an outdated class balance. We formalise temporal forgetting in FL with a per-phase metric isolated from protocol-level fluctuations and propose Flashback Continual Learning (FlashbackCL), a drop-in extension of Flashback with (i) temporally-decayed label counts; (ii) a device-aware replay buffer with Class-Balanced Reservoir Sampling (CBRS); and (iii) server-side active coreset curation on the public distillation set. The results show that FlashbackCL achieves 6.9% to 10.0% relative improvement relative to Flashback, on CIFAR-10 with 50 clients and three controlled temporal shift modes, while simultaneously reducing temporal forgetting by up to 68%. A 5-variant ablation identifies CBRS replay as the critical component. FlashbackCL also improves Flashback by 3.5 points on stationary CIFAR-100, suggesting that class-balanced replay regularises spatial heterogeneity as well as temporal shift.","short_abstract":"Federated Learning (FL) of foundation and edge models increasingly targets deployments where client data distributions drift over time, yet existing forgetting-mitigation methods assume each client's distribution is stationary. Flashback, the strongest recent FL method against cross-client (spatial) forgetting, uses mo...","url_abs":"https://arxiv.org/abs/2606.03939","url_pdf":"https://arxiv.org/pdf/2606.03939v1","authors":"[\"Mubarak A. Ojewale\",\"Adriana E. Chis\",\"Jorge M. Cortes-Mendoza\",\"Bernardo Pulido-Gaytan\",\"Horacio Gonzalez-Velez\"]","published":"2026-06-02T17:28:21Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.PF\"]","methods":"[]","has_code":false}