ks, such as powerful processability and unlimited storage space. However, some cloud services are provided by third parties such as Amazon AWS, Google Cloud, Microsoft Azure, etc. Users may hesitate to entrust their sensitive data to these entities, and rightly SO. To protect different data owners' privacy, the authors in [70] introduced a new efficient method that allowed all participants to publicly verify the veracity of the encrypted data. A Unidirectional Proxy Re-Encryption (UPRE) method was also adopted to lower the com- putation costs. A noise was further added to the encrypted data to preserve the private information while guaranteeing the effectiveness of ML training on cloud. A cloud-assisted privacy-preserving ML framework was developed in [224]. By using out- sourced ML algorithms, the cloud server first generated a model, then processed testing data from the network with the generated model in real-time. The proposed framework adopted a differential privacy method of performing privacy-preserving data analysis and homomorphic encryption in order to conduct valid operations over encrypted data. FL allowed decoupling of data provision and ML model aggregation and shows promise as a framework for addressing privacy problems for distributed ML [178]. It enables the users to cooperatively learn a global model without sacrificing data privacy directly. The information transmitted for FL consists of minimal updates to improve a particular machine learning model. However, the design of FL still needs the protection of parameters as well as investigations on the tradeoffs between the privacy-security-level and the system performance. The study [62] suggested that FL could expose intermediate results such as stochastic gradient descent, and the transmission of these gradients may actually leak private information when exposed together with a data structure. It is still possible for adversaries to reconstruct the raw data approximately, especially when the architecture and parameters are not completely protect