arXiv:2608. 01290v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) such as Chronos have demonstrated strong forecasting capabilities across domains, yet adapting them to institutionally fragmented settings, where data cannot be centralized due to regulatory, competitive, or sovereignty constraints, remains unexplored.
arXiv:2608. 04695v1 Announce Type: cross Abstract: Federated adaptation of time-series foundation models (TSFMs) is attractive for building energy forecasting because meter data are private, distributed, and highly non-IID.
arXiv:2607. 04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training.
Federated adaptation of time-series foundation models (TSFMs) is attractive for building energy forecasting because meter data are private, distributed, and highly non-IID. However, a single parameter-sharing strategy is unlikely to serve all pretrained TSFMs or building clients: fully shared adapters can suppress building-specific temporal behavior, while fully local adaptation discards cross-building transfer.
arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.
The paper introduces Smooth‑L1 regularized Echo State Networks (SL1ESN) that encourage sparsity in the readout layer while remaining differentiable for gradient‑based training. Using a twice‑differentiable Smooth‑L1 approximation, SL1ESN is trained centrally and via a second‑order federated aggregation scheme, achieving 75–95% sparsity across seven time‑series classification benchmarks. The resulting sparse models reduce storage, inference cost, and communication overhead, with only minor accuracy loss compared to dense baselines.
arXiv:2607. 24218v1 Announce Type: cross Abstract: Federated learning has emerged as a promising paradigm for spatio-temporal forecasting (STF), enabling collaborative model training without sharing raw observations.
FL-MAESTRO is a multi‑agent orchestrator that uses three specialized large language model agents to jointly decide the communication topology, per‑client resource allocation, and aggregation rule in each federated learning round. A coordinator merges the agents’ analyses, and a non‑LLM feasibility check validates the decision before execution. By filtering out clients whose updates would never be aggregated, the system eliminates the main source of wasted round energy in volatile edge networks and works across heterogeneous device classes without per‑class energy models, achieving comparable accuracy to the best energy‑aware baseline while reducing wasted energy from over a third to near zero on a non‑IID CIFAR‑10 benchmark.
The paper proposes a new federated learning approach called FedALS that reduces communication costs by varying aggregation frequencies across model layers. It derives tighter generalization bounds for one‑round and multi‑round federated learning, linking these bounds to local updates and data heterogeneity. Based on representation‑learning insights, the authors argue that infrequent aggregation of early layers and more frequent aggregation of final layers yields more generalizable models, especially in non‑iid settings, and demonstrate the method’s effectiveness experimentally.
arXiv:2606. 14416v1 Announce Type: new Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.
The paper investigates how model initialization affects federated short‑term load forecasting (STLF) when client data are heterogeneous. It introduces two strategies: a global pretrained initialization using auxiliary public load data to reduce client drift, and a local sequential initialization (SLIAvg) that lets clients start from progressively adapted models each round. Experiments on real smart‑meter data show these methods improve convergence and lower forecasting errors while remaining compatible with existing federated learning frameworks.
The paper introduces a latent information sharing scheme for federated learning that mitigates client drift by sharing a small amount of hidden‑layer activations. The authors demonstrate both theoretically and empirically that this approach improves training efficiency while maintaining convergence guarantees and data privacy. Compared to existing methods such as FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, the proposed method achieves higher model accuracy within a fixed round budget without adding significant communication overhead.