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/.