(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
Task management platforms are widely used to coordinate work across distributed teams. However, most existing systems focus primarily on monitoring workflows rather than predicting potential delays. In modern multi-tenant cloud environments, centralized machine learning approaches may also introduce privacy concerns because task data from multiple organizations must be aggregated This paper proposes a decentralized predictive intelligence framework for multitenant task management platforms using federated optimization. In the proposed system, each tenant trains a local machine learning model using its own task data while a global model is constructed through federated aggregation of model parameters. This approach allows organizations to benefit from collaborative learning while preserving data privacy. Experimental evaluation demonstrates that the proposed framework improves task delay prediction accuracy while maintaining strict data isolation across tenants