Cross-User Memory Learning and Selective Transfer: A Privacy-Preserving Framework for Experience Abstraction, Collective Intelligence, and Recipient-Adaptive Knowledge Distribution in AI Systems

Contemporary artificial intelligence systems increasingly employ persistent memory mechanisms to retain user preferences, interaction histories, task-specific experiences, and contextual information. Although these mechanisms improve personalization and continuity, most existing memory architectures primarily optimize knowledge accumulation within individual user boundaries. Consequently, valuable experiences discovered through prolonged interactions with one user are generally unavailable to other users, even when these experiences could provide substantial benefits in similar tasks or environments. This paper proposes Cross-User Memory Learning and Selective Transfer (CML-ST), a conceptual framework that enables artificial intelligence systems to transform selected individual interaction experiences into reusable knowledge resources and selectively distribute them to other users under privacy, authorization, reliability, and contextual compatibility constraints. Unlike conventional memory sharing, the proposed framework distinguishes between original personal memories, abstracted transferable experiences, and recipient-adapted knowledge. It introduces a coordinated architecture comprising Individual Memory Acquisition, Experience Abstraction, Transferability Assessment, Privacy and Authorization Governance, Selective Memory Routing, Recipient-Adaptive Integration, and Feedback-Driven Memory Evolution. A central mechanism is Selective Memory Distribution, through which the system determines not only whether an experience is potentially reusable, but also which recipients should receive it, under what conditions, and in what adapted form. The framework further introduces a multi-dimensional transfer decision model incorporating contextual compatibility, evidence quality, expected utility, privacy risk, authorization validity, and negative transfer probability. The paper identifies several potential advantages, including collective experience accumulation, reduced repeated problem-solving effort, accelerated knowledge acquisition, adaptive interaction strategies, cross-domain knowledge reuse, and improved long-term intelligence development. It also examines critical challenges involving privacy leakage, memorization attacks, biased experience aggregation, consent withdrawal, knowledge poisoning, distribution inequality, and the difficulty of verifying whether an abstracted experience is genuinely transferable. Importantly, the framework does not assume that anonymization alone guarantees privacy or that transferring an experience necessarily improves recipient performance. Instead, it establishes selective transfer as a constrained, measurable, and reversible learning process. This research provides a conceptual foundation for developing collective memory intelligence infrastructure in which individual learning and collective learning can reinforce one another without requiring unrestricted access to personal interaction histories.