Sustainable Federated Continual Learning for environmental monitoring: a systematic review

Introduction The use of distributed sensing infrastructures is increasingly becoming a key approach to environmental monitoring that is required to be mobile, flexible and adaptable to non-stationary conditions, preserve data privacy and consume as few resources as possible. The decentralized training paradigm provided by Federated Learning together with the ability of adapting sequentially over time without forgetting from Continual Learning has resulted in a concept of Federated Continual Learning (FCL) which offers a promising approach for privacy preserving environmental sensing systems that are capable of learning over time without forgetting. This systematic review provides an in-depth synthesis of the latest research, progress, environmental applications, sustainability, trustworthiness and future directions of Federated Continual Learning (FCL) for environmental monitoring. Methods A total of 90 peer-reviewed publications were identified by conducting a systematic literature search using pre-defined inclusion/exclusion criteria, quality assessment, bibliometric analysis and qualitative evidence synthesis with the framework of PRISMA 2020. Results The synthesized evidence suggests that Federated Learning has come of age in terms of optimization, communication complexity, robustness, and personalization, but Federated Continual Learning is still in its infancy with few applications in the real world. Typically, sustainability, trustworthiness, privacy, fairness, personalization and explainability are explored separately and not in parallel. Although most applications have been single-shot Federated Learning systems, without genuine continual adaptation, isolated examples include some applications from air quality to water quality, agriculture to disaster monitoring and remote sensing. Seven cross-cutting research gaps are identified: disjointed integration of research directions, few deployments in the real world, absence of standardized evaluation, unsolved multi-objective optimization, poor scaling in the long term, weak sustainability-trustworthiness coupling, and poor cross-domain generalization. Discussion It is not just about the evolution of single algorithmic breakthroughs anymore, but actual realization of Federated Continual Learning systems that can effectively assist in continual adaptation while ensuring communication efficiency, sustainability, trustworthiness, scalability and long-term deployment under real environmental conditions.