<![CDATA[The advancement of distributed computing has enabled the use of multi-cloud and serverless computing, which are beneficial due to their flexibility, scalability, and cost efficiency. There are, of course, pertinent challenges associated with these computing paradigms, such as resource heterogeneity, cold-start latency, vendor lock-in, and privacy. Recent trends in Federated Learning (FL) and Deep Reinforcement Learning (DRL) offer promise in tackling these challenges. FL systems enable decentralised, privacy-preserving model training across heterogeneous systems, while DRL systems enable adaptive models for real-time decision-making to optimise system resources and improve performance. This Systematic Literature Review (SLR) covers the years 2020 to early 2026 and examines the intersection of FL and DRL in multi-cloud serverless computing, following the PRISMA methodology. A primary analysis of 50 quality studies was undertaken to answer four privacy-related resource management questions. The results demonstrated FL increases privacy and scalability utilizing decentralised training. Consolidating the Federated DRL and Multi-Agent stacks increases the system by obtaining a better trade-off and optimization among latency, energy, and operational efficiency. However, a few gaps still exist, such as the absence of a more holistic framework, elusiveness in cross-system integration and collaboration, and a lack of concrete real-world applications. More work is needed to build a cohesive Federated Learning framework to improve sustainability and security in the multi-cloud, serverless systems of the future. Beyond a systematic literature review, this study provides a comparative synthesis of FL–DRL-based approaches and proposes a conceptual orchestration framework as a foundation for intelligent resource allocation in serverless multicloud environments.]]>
