Challenges in Federated Learning for Resource-Constrained IoT Environments: Energy Efficiency, Privacy, and Statistical Heterogeneity

Due to the extensive Internet availability and interconnections of sensing devices, new sensor-equipped or Internet-of-Things (IoT) devices with increased processing and computing capabilities are generating enormous volumes of data. With the use of Machine Learning (ML) techniques, this data may be preprocessed, categorized, and used to forecast upcoming occurrences. However, because data is transferred to and analyzed via a central server, typical ML algorithms have high communication cost, processing latency, privacy concerns, and security problems. To overcome these issues, Federated Learning (FL) employs a decentralized learning strategy in which each client is taught locally using its available data and learning from the global model. Implementation and scalability issues for FL approaches arise from the different computing resource capacities of clients in large-scale networks. In this paper, we discuss open problems and future directions in the FL domain related to devices with limited resources, such as memory, bandwidth, and energy efficient algorithms. We also explore recent real-world applications of FL, highlighting the difficulties of implementing FL algorithms in resource-constrained IoT environments, and explore open issues in the FL domain. The main contributions of this study include the challenges and how to deal with them and detailed discussion about energy-efficient algorithms addressing straggler clients, privacy preservation, and handling statistical heterogeneity. For energy-efficient algorithms, alternative neural network architectures such as Spiking Neural Networks (SNNs) is discussed, Mitigating the impact of straggler clients can be achieved by capable clients based on available resources and developing asynchronous training techniques. Privacy concerns can be addressed through privacy-preserving mechanisms like Secure Multiparty Computation (SMC) or Differential Privacy (DP). Handling statistical heterogeneity requires investigating data preprocessing techniques, developing adaptive algorithms, and exploring approaches that explicitly model heterogeneity. By focusing on these challenges, researchers can advance the field of FL and enable its wider adoption in various domains and applications. The findings and recommendations of this study provide valuable insights for future research in FL.