# Federated Learning for Edge Computing: A Survey
## 3 Federated Learning
### Privacy and Security  
With the development of big data technologies and high-performance computing, machine learning has opened up new possibilities for data-intensive science in the multidisciplinary field of agri-technologies. In traditional ML, a centralized server's computing resources and training data are essential to the models' effectiveness and accuracy. Briefly put, in traditional ML, user data is kept on a central server and used for testing and training procedures that lead to the development of sophisticated ML models. In general, centralization-based ML approaches face several difficulties, particularly in terms of user data security and privacy as well as computational performance and time [62]. The security and privacy of the personal data collected are considered using AI-based technologies, following several legislative regulations protecting citizens' data and privacy, such as the General Data Protection Regulation (GDPR) [63] in the European Union. United States has the California Consumer Privacy Act (CCPA) [64] and the Personal Data Protection Act (PDPA) [65] in Singapore. This issue has become critical. They demand that personal data be processed transparently with a clearly stated purpose and the data subject's consent. The topics of security and protection of personal data are nowadays the subject of more and more research. The same is true for FL, where many studies focus on security and information leakage in different segments and parts of FL. However, studies have demonstrated that adversaries can still compromise industrial applications and the manufacturing sector, medical and healthcare data in wearable devices, logs of personal data, and the decision-making of industrial robots or autonomous vehicles by improperly using shared parameters [66]. Often, the clients are the victims of an attack when the attacker tries to obtain the client's private data. FL was designed not only to train different models in a data-efficient manner but also to ensure user privacy, as their input data remains on the device and only individual weights and parameter gradients are shared. Because clients send updates to their local training parameters to the FL server, they are the most vulnerable attack surface in the FL system. In FL, the mention of the security and whether the sharing of gradients can be broken. Using these shared gradients, it is then possible to reassemble the image back into its original form or at least a similar one. Even multi-frame federated averaging on realistic architectures does not guarantee the privacy of all user data. Model aggregation is one of the most common privacy mechanisms in FL, which trains a global model by summarizing model parameters from all parties to avoid transferring the original data in the training process. In [67,68], potential attack routes are discussed, the ability to reconstruct the input to any fully connected layer analytically is examined, a general attack based on optimization using cosine similarity gradients is proposed, and its applicability to various architectures and scenarios is discussed. According to experimental findings, privacy is not a property of inherent collaborative learning algorithms such as FL, and secure applications that potentially leak private information need to be thoroughly examined on a case-by-case basis. This reconstruction attack aims to iteratively add the generated small noises to the attack sample so that the generated gradient from this reconstructed attack sample approximates the actual gradient computed on the local training data [69]. The attackers can reconstruct the private local training data by analyzing the shared local gradient update parameters or the weight update vector. As more and more demands are placed on privacy and security, current methods and algorithms are constantly improving and updating. Different options are emerging to address the gradient mentioned above attacks. Encryption of gradients and parameters is dealt with quite a lot nowadays, and this topic is discussed in detail.