The challenge of understanding and enforcing privacy norms isn’t new, but it’s become more important in recent years due to the rapid advancement of AI.
The shifting dynamics of the digital world have created a slew of privacy issues for both large and small enterprises. As a result, they are under increased pressure to improve their processes and strategies. The sheer number of data available today contributes to much of the strain. Now, without the aid of privacy-enhancing technologies (PETs), it is just beyond human competence to successfully handle and protect privacy.
As a result, there have been explosions of adaptive machine learning (ML) algorithms that can sift through mountains of data while modifying their behavior in real-time as new data streams are fed into them. While machine learning is essential for exploiting and learning from large amounts of data, it can also pose privacy challenges. In reality, traditional machine learning requires data to be maintained on a centralized server for analysis, which includes moving data to cloud settings, which brings up a slew of security and privacy concerns.
Taking it to the edge
Due to these privacy and security concerns, ML technology that can work in a way that protects user privacy is gaining traction, which is why federated learning (FL) has acquired so much traction.
A global model is built in a central server with decentralized, federated learning, and the data used to train this model is disseminated across edge devices. While the data remains with the owner, it is nonetheless used to provide insights at a central level. Federated learning is a great example of the new generation of edge computing, which brings computation and storage closer to the data source.
Load distribution
While federated learning has numerous advantages, it also has certain drawbacks. Not least, the system relies on constant communication between the nodes to function during the learning process. As a result, sufficient local computing power and memory are required, which may have an impact on the user’s experience and bandwidth while exchanging machine learning model parameters in real-time. Fortunately, with the introduction of technologies like 5G, today’s communications infrastructure is more than capable of handling this.
Also Read: Four Strategies for Designing an Effective Security Policy
Closing the loopholes
Federated learning facilitates important concerns in machine learning, such as data security, data access rights, and access to heterogeneous data, by allowing several actors to develop a standard, robust ML model without sharing data. While federated learning will likely become an important part of the modern marketing technology stack, it should be deployed with caution. Despite the fact that such a technique is by its very nature a step forward in data privacy, privacy-by-design principles must be followed at all times.
Privacy-by-design incorporates privacy into the design and management of IT systems, networked infrastructure, and business activities in a proactive manner. When federated learning is used in conjunction with other privacy methods, such as secure multi-party computation, differential privacy, and quantitative assessment, privacy concerns are reduced. As a result, federated learning is a matter of filling in the gaps in order to comply with increasingly strict privacy regulations.
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