Decentralized　federated　learning　(DFL)　is　a　novel　distributed　machine-learning　paradigm　where　participants　collaborate　to　train　machine-learning　models　without　the　assistance　of　the　central　server.　The　decentralized　framework　can　effectively　overcome　the　communication　bottleneck　and　single-point-of-failure　issues　encountered　in　federated　learning　(FL).　However,　most　existing　DFL　methods　may　ignore　the　communication　resource　constraints　of　the　system.　This　may　result　in　these　methods　unsuitable　for　many　practical　scenarios　because　the　given　resource　constraints　cannot　be　guaranteed.　In　this　article,　we　propose　a　novel　DFL,　called　DFL　with　adaptive　compression　ratio　(AdapCom-DFL),　that　can　adaptively　adjust　the　compression　ratio　of　transmission　data　to　keep　the　communication　latency　within　the　constraint.　Furthermore,　we　propose　a　communication　network　topology　pruning　approach　to　reduce　communication　overhead　by　pruning　poor　links　with　low　data　rates　while　ensuring　the　convergence.　Additionally,　a　power　allocation　approach　is　presented　to　improve　the　performance　by　reallocating　the　power　of　communication　links　while　complying　with　the　communication　energy　constraint.　Extensive　simulation　results　demonstrate　that　the　proposed　AdapCom-DFL　with　network　pruning　and　power　allocation　approach　achieves　better　performance　and　requires　less　bandwidth　under　the　given　resource　constraints　compared　with　some　existing　approaches.