To　reduce　uploading　bandwidth　and　address　privacy　concerns,　deep　learning　at　the　network　edge　has　been　an　emerging　topic.　Typically,　edge　devices　collaboratively　train　a　shared　model　using　real-time　generated　data　through　the　Parameter　Server　framework.　Although　all　the　edge　devices　can　share　the　computing　workloads,　the　distributed　training　processes　over　edge　networks　are　still　time-consuming　due　to　the　parameters　and　gradients　transmission　procedures　between　parameter　servers　and　edge　devices.　Focusing　on　accelerating　distributed　Convolutional　Neural　Networks　(CNNs)　training　at　the　network　edge,　we　present　DynaComm,　a　novel　scheduler　that　dynamically　decomposes　each　transmission　procedure　into　several　segments　to　achieve　optimal　layer-wise　communications　and　computations　overlapping　during　run-time.　Through　experiments,　we　verify　that　DynaComm　manages　to　achieve　optimal　layer-wise　scheduling　for　all　cases　compared　to　competing　strategies　while　the　model　accuracy　remains　untouched.