Data is readily available with the growing number of smart and IoT devices. Industries of different sectors follow technological advancement to be benefited from data sharing. However, application-specific data is available in small chunks and distributed across demographics. Additionally, sharing data online brings serious concerns and poses various security and privacy threats. To address these issues, federated learning (FL), a secure and collaborative learning paradigm, would be suitable, which brings the machine learning model to the data owners. Unfortunately, FL is prone to poisoning and inference attacks in presence of malicious users and curious servers. This work proposes a permissioned blockchain based federated learning framework, called PrivateFL (Privacy-Preserving Federated Learning Framework). PrivateFL replaces the central server with a Hyperledger Fabric network, to prevent inference attacks. Further, we propose VPSA (Vertically Partitioned Secure Aggregation) tailored to PrivateFL framework, which performs robust and secure aggregation. PrivateFL facilitates multi-tenancy for learning different machine learning models. Theoretical analysis proves that the system is resistant against inference attacks, even if n-1 peers are compromised. A secure prediction mechanism is also proposed to securely query a global model and protecting its intellectual property rights. Experimental evaluation shows that PrivateFL performs better than the traditional (centralized) learning systems and converges faster, while capable enough to detect malicious updates.