一句话：提出从联邦学习视角构建可自组织、自适应、去中心化且保护隐私的智能软件系统的研究愿景。
现代软件系统日益部署在大规模、异质且持续演化的环境中，依赖静态配置和集中管理的传统架构难以应对运行时变化、多样数据分布和不同用户上下文。本文提出一项研究愿景：从联邦学习视角推进自自适应与去中心化智能软件系统的基础，使系统能够快速部署、个性化适应并持续演化。与聚焦运行时配置的传统自自适应系统不同，作者将自适应概念扩展到智能导向的适应，并探索支持系统自组织、适应与维持的架构和原则。该研究展望新一代去中心化智能软件系统，使其能自主维持可扩展性、韧性与隐私，同时保持长期适应性和个性化。论文属于研究议程/立场性工作，未报告具体实现或实证评估。
Modern software systems are increasingly deployed in large-scale, heterogeneous, and continuously evolving environments. However, traditional software architectures that rely on static configurations and centralized management are insufficient in these settings, as systems must dynamically adapt to runtime changes, diverse data distributions, and varying user contexts. We aim to advance the foundations of self-adaptive and decentralized intelligent software systems capable of rapid deployment, personalized adaptation, and continuous evolution in large-scale heterogeneous environments. In contrast to conventional self-adaptive systems that focus on runtime configuration, our notion of it extends to intelligence-oriented adaptation. Building on this, we explore the architectures and principles that enable these systems to self-organize, adapt, and maintain in dynamic environments. This research envisions a new generation of decentralized intelligent software systems that can autonomously maintain scalability, resilience, and privacy while sustaining long-term adaptability and personalization.