Federated　learning　is　a　machine　learning　training　paradigm　that　enables　model　training　under　data　privacy　constraints.　Its　distinguishing　capability　is　the　ability　to　train　models　without　requiring　participating　parties　to　share　private　data.　However,　model　training　and　aggregation　in　federated　learning　is　communication　and　computation　intensive.　Relying　solely　on　cloud　computing　is　insufficient　to　balance　the　trade-offs　in　model　aggregation　and　training　given　the　distributed　nature　of　federated　learning.　Coordination　of　Computing　and　Networking　(CNC),　an　emerging　infrastructure,　incorporates　massive　cloud,　edge　/　end　computing　into　the　communication　network　enabled　by　ubiquitous　6G　connectivity.　This　forms　an　integrated　infrastructure　encompassing　both　networking　and　ubiquitous　computing.　CNC　can　improve　performance　for　computing-intensive　workloads,　time-sensitive　applications　and　distributed　computing　tasks　via　on-demand　computing　invocation.　This　paper　studies　how　to　exploit　the　ubiquitous　distributed　computing　resources　of　computational　networks　to　facilitate　federated　learning　model　training　and　aggregation.　Challenges　addressed　include　communication　overhead　from　repeated　model　aggregations　across　participating　nodes　and　the　central　node,　computing　overhead　for　local　model　training　at　participating　nodes　and　global　model　aggregation　at　the　central　node,　and　improving　computational　network　resource　utilization　efficiency　throughout　this　process.　©　2023　IEEE.