Modern industrial systems, from wind-farm monitoring to economic indicators like GDP generate vast amounts of time series data from diverse sources. These data streams are sampled at varying and often inconsistent frequencies, presenting challenges for accurate forecasting. Furthermore, in many real-world scenarios, data are distributed across nodes or tasks, introducing complications due to heterogeneity across tasks. Existing forecasting approaches typically address frequency misalignment and decentralized learning as separate problems, limiting their ability to model real-world deployments effectively. We propose CrossFreqNet, a unified multitask encoder–decoder architecture that addresses both challenges: (i.) integrating multi-frequency data streams without the need of up or down sampling to match frequency, preserving signal integrity and (ii.) introducing GradBal, a gradient-balancing mechanism that mitigates learning conflicts caused by task heterogeneity and promoting stable convergence across tasks in a distributed learning environment. Across four public benchmarks and one industrial dataset, our model reduces forecasting errors by up to 72% over the best multi-task baseline (UniTS) and up to 48% over PCGrad, a SOTA gradient conflict mitigation method. Code is made available at https://github.com/arc-arnob/TS-MTL/.

Collaborative efforts in Predictive Maintenance and Control can be beneficial for manufacturers and customers in industrial environments. However, these efforts are challenged by the need for multi-dimensional sharing of information about the same type (horizontal) and piece (vertical) of equipment, privacy restrictions and the presence of heterogeneous data distributions across participants.

Existing solutions have addressed some of these challenges for forecasting or different purposes but there lacks a comprehensive approach that handles all of them for time series forecasting. To solve this problem, we introduce Time-series-based Personalized Hybrid Federated Learning (TPHFL), a hybrid federated learning (FL) strategy that combines Horizontal FL and Vertical FL to enable multi-level knowledge exchange while preserving data privacy. All participants use a personalization mechanism to make predictions that better suit their underlying data distribution.

Our approach employs a distributed model to handle vertical privacy constraints and addresses data heterogeneity across equipment through a personalisation mechanism. Through extensive experiments on four public and one industry-specific datasets, we show that TPHFL outperforms independent learning scenarios by 27.2%, providing a strong incentive for parties to collaborate.

We demonstrate the effectiveness of personalisation by showing an accuracy improvement of up to 42.7% when comparing TPHFL with personalisation to TPHFL without personalisation, and 32.7% when comparing traditional HFL methods to HFL with personalisation. Additionally, we evaluate a different configuration for personalisation and perform a detailed hyperparameter analysis to better understand the behaviour of TPHFL in different contexts.