Title: P2PRISM - Peer to peer learning with individual prism for secure aggregation. Abstract: Federated learning (FL) has made collaboration between nodes possible without explicit sharing of local data. However, it requires the participating nodes to trust the server and its model updates, the server itself being a critical node susceptible to failure and compromise. A loss of trust in the server and a demand to aggregate the model independently for oneself has led decentralized peer-to-peer learning (P2PL) to gain traction lately. In this paper, we highlight the never before exposed vulnerabilities of P2PL towards malicious attacks and how P2PL behaves differently from FL in such a malicious environment. We then present a robust defense - P2PRISM as a secure aggregation protocol for P2PL.