A Comprehensive Survey of Federated Model Evolution in Open Environments
Abstract
With the rapid development of the Internet of Things and edge computing, federated learning (FL) has become an important technique for collaborative intelligent modeling across distributed nodes. However, in real-world open environments, federated systems must continuously cope with dynamically joining devices, evolving service requirements, and privacy-driven data deletion, making traditional federated training paradigms built on static assumptions increasingly inadequate. To address this challenge, this paper provides a systematic survey of key research progress on federated learning from the perspective of continuous model evolution in open environments. Specifically, we analyze three core problems faced by federated systems in open environments: how to efficiently adapt to newly arrived nodes or data distributions, how to preserve performance on historical tasks during continual training, and how to effectively remove the contribution of specific data or nodes under privacy and compliance requirements. We then organize the literature around three major technical routes, namely federated domain adaptation, federated continual learning, and federated unlearning. On this basis, we further summarize commonly used evaluation metrics and mainstream experimental benchmarks in this area. Finally, we discuss the major challenges and future research directions of federated model evolution in open environments, with the goal of providing a systematic reference for building adaptive…
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