Self-adaptation is a widely accepted approach to deal with uncertainties that are difficult to anticipate before deployment. We focus on architecture-based adaptation that relies on a feedback loop that reasons over architectural models of the system at runtime to make adaptation decision. In particular, we study decentralized self-adaptive systems where self-adaptation is realized through multiple coordinating feedback loops. Such decentralization is crucial for systems where adaptation decisions cannot be made in a centralized way, such as in large scale Internet of Things (IoT). State of the art in this area is limited to either conceptual ideas or solutions dedicated to particular settings. This paper outlines a research project targeting the research question: ``how to model and realize decentralized feedback loops that are capable to guarantee compliance of system goals in an efficient way despite uncertainties the system faces?'' We plan to answer this question in two stage. First, we study commonalities and variability of decentralized self-adaptive systems leveraging on patterns and coordination mechanisms, and reify our insights in a framework. Second, we study language support for the design and implementation of decentralized self-adaptation, capitalizing on the outcome of the first stage. To ensure guarantees for the qualities we will found our work on formal techniques. To ensure efficiency, we will combine statistical techniques with machine learning. We plan to validate the research results in two domains: IoT and multi-cloud systems.