task failure prediction in cloud computing systems using federated learning

As cloud data centers grow in scale andcomplexity, ensuring high service reliability and minimizingfailures have become critical challenges. despite technologicaladvances, failures due to hardware and software issues persist,disrupting tasks, wasting resources, and impacting servicereliability. accurately predicting task or job failures before theyoccur is essential to reducing downtime and unnecessaryresource usage. traditional fault-tolerance methods likecheckpointing and replication are insufficient for the complexityof modern systems. consequently, machine learning and deeplearning techniques have been adopted to analyze system logsand predict failures more accurately. federated learningfurther enhances this by enabling decentralized data analysisacross nodes, preserving privacy while improving predictionaccuracy through collaborative learning. in this paper, wepropose a fault prediction mechanism based on federatedlearning and a deep neural network to identify patterns leadingto task failures. our model achieved a high prediction accuracyof 95.3%, making it a robust solution for failure prediction incloud computing environments.