Federated learning (FL) refers to a system of training and stabilizing local machine learning models at the global level by aggregating the learning gradients of the models. It reduces the concern of sharing the private data of participating entities for statistical analysis to be carried out at the server. It allows participating entities called clients or users to infer useful information from their raw data. As a consequence, the need to share their confidential information with any other entity or the central entity called server is eliminated. FL can be clearly interpreted as a privacy-preserving version of traditional machine learning and deep learning algorithms. However, despite this being an efficient distributed training scheme, the client’s sensitive information can still be exposed to various security threats from the shared parameters. Since data has always been a major priority for any user or organization, this article is primarily concerned with discussing the significant problems and issues relevant to the preservation of data privacy and the viability and feasibility of several proposed solutions in the FL context. In this work, we conduct a detailed study on FL, the categorization of FL, the challenges of FL, and various attacks that can be executed to disclose the users’ sensitive data used during learning. In this survey, we review and compare different privacy solutions for FL to prevent data leakage and discuss secret sharing (SS)-based security solutions for FL proposed by various researchers in concise form. We also briefly discuss quantum federated learning (QFL) and privacy-preservation techniques in QFL. In addition to these, a comparison and contrast of several survey works on FL is included in this work. We highlight the major applications based on FL. We discuss certain future directions pertaining to the open issues in the field of FL and finally conclude our work.

The safeguarding of digitized data against unwanted access and modification has become an issue of utmost importance as a direct result of the rapid development of network technology and internet applications. In response to this challenge, numerous secret image sharing (SIS) schemes have been developed. SIS is a method for protecting sensitive digital images from unauthorized access and alteration. The secret image is fragmented into a large number of arbitrary shares, each of which is designed to prevent the disclosure of any information to the trespassers. In this paper, we present a comprehensive survey on SIS schemes along with their pros and cons. We review various existing verifiable secret image sharing (VSIS) schemes that are immune to different types of cheating. We have identified various aspects of developing secure and efficient SIS schemes. In addition to that, a comparison and contrast of several SIS methodologies based on various properties is included in this survey work.We also highlight some of the applications based on SIS. Finally, we present open challenges and future directions in the field of SIS.