Federated time-series forecasting is increasingly important in applications where temporal data are distributed across privacy-sensitive clients, such as environmental monitoring, smart cities, and industrial sensing. A central limitation of current continual-learning protocols is that they usually assume predefined task boundaries, whereas deployed forecasting systems receive an uninterrupted stream and must decide autonomously when adaptation is necessary. This paper addresses catastrophic forgetting in task-free federated forecasting under concept drift. We introduce a drift-aware continual-learning protocol in which each client monitors prediction-window RMSE, calibrates a robust median threshold from the offline base period, and triggers adaptation only after persistent degradation. To mitigate forgetting, we propose a TwoTierReplay mechanism that separates fixed offline representative memory from online memory. The offline tier preserves long-term knowledge learned during the base phase, while the online tier captures recent distributional changes after drift. Experiments on the Beijing Multi-Site Air- Quality dataset with 12 clients, three forecasting targets, and three horizons show that TwoTierReplay achieves the best stability-plasticity score in all nine target-horizon configurations. It improves over the strongest baseline by approximately 11.4%. These results suggest that explicit temporal structuring of replay memory is a practical and effective mechanism for task-free federated continual forecasting.
Figures & tables
Fig. 1: Task-free federated continual-forecasting protocol. Each client maintains fixed offline and bounded online replay memories locally. A persistent drift alarm at any client triggers local adaptation at all K clients; the server aggregates only model parameters and rebroadcasts the updated global model.
Fig. 2: TwoTierReplay memory and local-update flow. The non-evictable offline tier stores representative base-period samples, whereas the bounded FIFO tier stores recently revealed stream samples. At a drift event, current samples and replay samples from both tiers are used for local adaptation.
Component
Setting
Clients
12 monitoring stations
Targets
TEMP, PM 2.5 , WSPM
Horizons
p=6,12,24 hours
Input length
n=12 hours
Model
LSTM, hidden dimension 64
Base training
FedAvg, 100 rounds, 1 local epoch
TABLE I: Main experimental configuration.
Method
TEMP-6
TEMP-12
TEMP-24
PM 2.5 -6
PM 2.5 -12
PM 2.5 -24
WSPM-6
WSPM-12
WSPM-24
Avg.
Static
0.2413
0.2430
0.2503
0.1582
0.1620
0.1660
0.1504
0.1569
0.1637
0.1880
Naive
0.2139
0.2292
0.2432
0.1614
0.1642
0.1686
0.1767
0.1678
0.1723
0.1886
EWC
0.2043
0.2285
0.2425
0.1532
0.1561
0.1619
0.1512
0.1580
0.1638
0.1799
LwF
0.2139
0.2292
0.2434
0.1567
0.1606
0.1631
0.1742
0.1741
0.1801
0.1884
SI
0.2139
0.2293
0.2430
0.1588
0.1605
0.1639
0.1553
0.1580
0.1632
0.1829
NormalReplay
0.1966
0.2099
0.2186
0.1502
0.1562
0.1637
0.1577
0.1627
0.1680
0.1760
TABLE II: Mean signed stability–plasticity score, SP=RMSEstream+AFbase , over three random seeds. The Avg. column is the arithmetic mean across the nine target-horizon configurations. Lower values are better.
Federated continual learning must integrate new tasks over time without losing earlier-task knowledge. Most existing methods attach an anti-forgetting mechanism to the client-trained, server-aggregated loop of federated learning, which holds back new learning to preserve earlier knowledge and burdens resource-constrained clients. We propose ReSCENE, which structurally mitigates catastrophic forgetting by having each client upload a small condensed surrogate of its local data while the server keeps the surrogates of past tasks and trains the global model on them together with the current task surrogates. For efficient server memory, we introduce temporal herding, which selects the more recent surrogates from the pool accumulated over a task into a compressed buffer. Our study provides a theoretical analysis showing that this buffer can represent the original task data more closely than full accumulation of all surrogates. Across CIFAR-10, CIFAR-100, and TinyImageNet, ReSCENE achieves the strongest accuracy over seven baselines, by up to 31.1 points of average accuracy, while requiring as little as 0.11× of the client computation and up to 179× less upload than the model-update baselines. ReSCENE further demonstrates its effectiveness when scaled to larger client populations and larger models while remaining efficient, which makes it a practical method for federated continual learning.
Federated continual learning (FCL) allows distributed autonomous fleets to adapt collaboratively to evolving terrain types across extended mission lifecycles. However, current approaches face several key challenges: 1) they use uniform protection strategies that do not account for the varying sensitivities to forgetting on different network layers; 2) they focus primarily on preventing forgetting during training, without addressing the long-term effects of cumulative drift; and 3) they often depend on idealized simulations that fail to capture the real-world heterogeneity present in distributed fleets. In this paper, we propose a lifecycle-aware dual-timescale FCL framework that incorporates training-time (pre-forgetting) prevention and (post-forgetting) recovery. Under this framework, we design a layer-selective rehearsal strategy that mitigates immediate forgetting during local training, and a rapid knowledge recovery strategy that restores degraded models after long-term cumulative drift. We present a theoretical analysis that characterizes heterogeneous forgetting dynamics and establishes the inevitability of long-term degradation. Our experimental results show that this framework achieves up to 8.3% mIoU improvement over the strongest federated baseline and up to 31.7% over conventional fine-tuning. We also deploy the FCL framework on a real-world rover testbed to assess system-level robustness under realistic constraints; the testing results further confirm the effectiveness of our FCL design.
Beining Wu, Jun Huang
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, 57006 SD
Federated continual learning (FCL) enables collaborative model training across distributed clients on sequentially arriving tasks without revisiting past data. However, existing approaches often suffer from catastrophic forgetting, rely on replay buffers or generative models that may violate privacy constraints, or assume knowledge of task identities during inference. We propose FedProTIP (Federated Projection-based Continual Learning with Task Identity Prediction), a replay-free FCL framework that maintains shared task-specific feature subspaces across clients. Each client extracts low-rank core bases from intermediate activations using randomized singular value decomposition, capturing dominant feature directions associated with the current task. These bases are transmitted to the server and aggregated to construct global task subspaces that capture shared feature directions across clients without requiring data sharing. During training, client updates are projected onto the orthogonal complement of previously learned subspaces to reduce cross-task interference and mitigate catastrophic forgetting. The learned subspaces are also reused during inference to estimate task identity via subspace relevance, enabling task-agnostic prediction without requiring explicit task labels. Experiments on CIFAR100, ImageNet-R, and DomainNet demonstrate that FedProTIP consistently outperforms state-of-the-art federated continual learning baselines while maintaining lower training time, memory footprint, and communication cost.
Seohyeon Cha, Huancheng Chen, Haris Vikalo
Department of Electrical and Computer Engineering The University of Texas at Austin