Survey of Federated Incremental Learning

Federated learning,with its unique distributed training mode and secure aggregation mechanism,has become a research hotspot in recent years.However,in real-life scenarios,local model training often faces new data,leading to catastrophic forgetting of old data.Therefore,effectively integrating federated learning with incremental learning is crucial for achieving sustainable development of federated ecosystems.This paper first conducts an in-depth investigation and analysis of federated incremental learning,exploring its concepts.Subsequently,it elaborates on federated incremental learning methods based on data,model,architecture,and multi-aspect joint optimization,while also categorizing and comparing various existing methods.Finally,building upon this foundation,it analyzes and summarizes future research directions for federated incremental learning,such as scalability,small sample handling,security,reliability,and multi-task scenarios.