FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models
This addresses a gap in federated learning for language models by enabling mutual knowledge transfer, potentially benefiting applications with heterogeneous model deployments, though it appears incremental as it builds on existing federated and transfer learning approaches.
The paper tackles the problem of mutual enhancement between server-based large language models (LLMs) and client-based small language models (SLMs) in federated learning, proposing FedMKT, which improves both LLMs and SLMs simultaneously, with empirical results showing performance boosts.
Recent research in federated large language models (LLMs) has primarily focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on transferring knowledge from server-based LLMs to small language models (SLMs) at downstream clients. However, a significant gap remains in the simultaneous mutual enhancement of both the server's LLM and clients' SLMs. To bridge this gap, we propose FedMKT, a parameter-efficient federated mutual knowledge transfer framework for large and small language models. This framework is designed to adaptively transfer knowledge from the server's LLM to clients' SLMs while concurrently enriching the LLM with clients' unique domain insights. We facilitate token alignment using minimum edit distance (MinED) and then selective mutual knowledge transfer between client-side SLMs and a server-side LLM, aiming to collectively enhance their performance. Through extensive experiments across three distinct scenarios, we evaluate the effectiveness of FedMKT using various public LLMs and SLMs on a range of NLP text generation tasks. Empirical results demonstrate that FedMKT simultaneously boosts the performance of both LLMs and SLMs.