  Text summarization is essential for information aggregation and demands large
amounts of training data. However, concerns about data privacy and security
limit data collection and model training. To eliminate this concern, we propose
a federated learning text summarization scheme, which allows users to share the
global model in a cooperative learning manner without sharing raw data.
Personalized federated learning (PFL) balances personalization and
generalization in the process of optimizing the global model, to guide the
training of local models. However, multiple local data have different
distributions of semantics and context, which may cause the local model to
learn deviated semantic and context information. In this paper, we propose
FedSUMM, a dynamic gradient adapter to provide more appropriate local
parameters for local model. Simultaneously, FedSUMM uses differential privacy
to prevent parameter leakage during distributed training. Experimental evidence
verifies FedSUMM can achieve faster model convergence on PFL algorithm for
task-specific text summarization, and the method achieves superior performance
for different optimization metrics for text summarization.
