Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning. It induces a large generalization gap, especially when the local data distribution amongst clients exhibits heterogeneity. To overcome communication blockages between clients and the central PS, we introduce the concept of collaborative relaying (ColRel) wherein the participating clients relay their neighbors' local updates to the PS in order to boost the participation of clients with poor connectivity to the PS. For every communication round, each client initially computes a local consensus of a subset of its neighboring clients' updates and subsequently transmits to the PS, a weighted average of its own update and those of its neighbors'. We optimize these weights to ensure that the global update at the PS is unbiased with minimal variance -- consequently improving the convergence rate. Numerical evaluations on the CIFAR-10 dataset demonstrate that our ColRel-based approach achieves a higher test accuracy over Federated Averaging based benchmarks for learning over intermittently-connected networks.