Implicit neural representations (INRs) have emerged as powerful tools for the continuous representation of signals, finding applications in imaging, computer graphics, and signal compression. Additionally, decentralized multi-agent systems are crucial in various applications, frequently leading to enhanced reliability and efficiencies in computation and communication. In this letter, we explore using multi-agent Lifelong Learning (LL) systems for learning INRs. We propose a rigorous problem setup and evaluation plan to investigate the efficacy of such systems compared to single-agent and multi-task learning baselines. Our research, conducted across varied dimensions, demonstrates promising results, thereby contributing a novel perspective to the realm of continual learning.