FLoKD: Educating Big Language Models With Federated Learning On Wireless Networks

Although large language models (LLM) have given significant results in the field of natural language processing in recent years, these models often require the collection of data in a central location. This approach reveals serious privacy concerns. Artificial intelligence researchers continue to develop new solutions to solve this problem.

The new technique called FLoKD aims to overcome these challenges by combining adaptive information transfer with federated learning:

• No need to be sent to central servers of the raw state of the client data, privacy is maintained
• Significant reduction of communication load on wireless networks and bandwidth efficiency is achieved
• Reduces the size of model parameters using low-row structures
• Adaptable distortion strategy optimizes learning quality according to the specific condition of each client

Federated learning approach allows clients to train model with local data in different locations, a central server only coordinates model updates. The most important contribution of FLoKD is that this process is practiced under limited resources such as**wireless networks. The information transfer technique is used to convert large models into smaller and efficient versions.

This research contributes to the prevalence of privacy-conscious AI applications and the realization of non-centered model training on mobile devices.