The　soaring　popularity　of　smart　devices　equipped　with　electrocardiograms　(ECG)　is　driving　a　nationwide　craze　for　predicting　heart　abnormalities.　Smart　ECG　monitoring　system　has　achieved　significant　success　by　training　machine　learning　models　on　massive　amounts　of　user　data.　However,　three　issues　arise　accordingly:　1)　ECG　data　collected　from　various　devices　may　display　personal　characteristic　variations,　leading　to　non-independent　and　identically　distributed　(non-i.i.d.)　data.　These　differences　can　impact　the　accuracy　and　reliability　of　data　analysis　and　interpretation;　2)　Most　ECG　data　on　smart　devices　is　unlabeled,　and　data　labeling　is　resource-consuming　as　it　requires　heavy-loaded　labeling　from　professionals;　3)　While　centralizing　data　for　machine　learning　can　address　above　issues　like　non-i.i.d.　data　and　labeling　difficulties,　it　may　compromise　personal　privacy.　To　tackle　these　three　issues,　we　introduce　a　novel　federated　semi-supervised　learning　(FSSL)　framework　named　FedECG　for　ECG　abnormalities　prediction.　Specifically,　we　adopt　a　pre-processing　module　to　better　utilize　the　ECG　data.　Next,　we　devise　a　novel　model　based　on　ResNet-9　in　FSSL　to　accurately　predict　abnormal　signals　from　ECG　recordings.　In　addition,　we　incorporate　pseudo-labeling　and　data　augmentation　techniques　to　enhance　our　implemented　semi-supervised　learning.　We　also　develop　a　model　aggregation　algorithm　to　improve　the　model　convergence　performance　in　federated　learning.　Finally,　we　conduct　simulations　on　a　real-world　dataset.　Experiments　demonstrate　that　FedECG　obtains　94.8%　accuracy　with　only　50%　of　the　data　labeled.　FedECG　achieved　slightly　lower　accuracy　than　traditional　centralized　methods　in　ECG　monitoring,　with　a　2%　reduction.　In　contrast,　FedECG　outperforms　the　state-of-the-art　distributed　methods　by　about　3%.　Moreover,　FedECG　can　also　support　unlabeled　data　and　preserve　data　privacy　as　well.　©　2023　The　Author(s)