# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Privacy and Security Related Studies in FL Framework  
The main advantage of a distributed FL framework is improved user data security, which traditional ML training approaches are unable to provide. Instead of sending the raw user data to the centralized servers, the FL devices are only required to send the locally trained ML model updates and hence enhancing the data security. However, recently it has been noticed that user information can still be leaked over an FL framework through new security attacks. Proper measures can be taken to boost the client and server-side privacy preservation while implementing more secure FL models [52]. The FL framework security and privacy issues can be classified as FL-device-side and FL-server-side. From the FL device point of view, adequate measures can be taken to elevate the device data-privacy-based perturbation techniques that are employed to protect the valuable user data's parameters by making them more elusive [53]. Otherwise, FL-devices can send the dummy model parameters to the server along with the true parameters to conceal the true parameters [54]. Similarly, various techniques can be added on the server-side for elevating the FL privacy. A proper aggregation technique for limiting the server's access to the individual device parameters, and using encryption techniques such as secure multi-party computation, are some of the common approaches. Techniques such as homomorphic encryption [55] and back-door defenders [56] can be used to discourage the malicious devices from stealing the FL models or from adding any backdoors in the global model. In addition to this various poisoning, attackers can elevate the security threats in the FL models [57; 58]. Poison attacks can occur when a malicious user introduces fake training data leading to improper training performances. In the case of FL, devices being in charge of the learning process can easily attack the training process. They can also insert the backdoors in the intermediate training states, leading to the theft of sensitive data from the other devices.  
Application of Blockchain Technology for Creating a Secure FL Platform  
Recently, blockchain-based methods have been increasingly used to develop more secure and privacy-protective FL architectures. In [59], authors designed a blockchain-based collaborative data sharing architecture with enhanced security for the distributed entities. This differential privacy is then incorporated into the FL process for creating a privacy-protective FL framework. In [60], a fully decentralized, blockchain-based federated learning framework was proposed for a vehicular networking system that ensures end-to-end trustworthiness. The FLchain paradigm, which integrates FL and blockchain technologies to create decentralized, secure, and privacy-enhancing distributed systems, is being increasingly investigated. In [61], authors studied the FLchain by analyzing the fundamental technologies, main challenges, and possible future directions. The applications of blockchain-enabled FL for the Internet of Vehicles (IoVs) are surveyed in [62]. Given the importance of non-terrestrial considerations, in [63], authors studied the integration of FL, blockchain, UAVs, and beyond 5G technologies together.