arXiv:2609.38626v1 Announce Type: cross Abstract: The rapid development of the Internet of Things (IoT) has led to the generation of vast amounts of data from sensors, prompting the need for advanced learning models to analyze this data for personalized services. Federated learning (FL) emerges as a solution, offering decentralized learning that preserves user privacy by building models on local
Enabling Efficient Client Selection in FL-as-a-Service for Multi-Application based Society 5.0 (arXiv:2609.38626v1) is a September 29, 2026 paper by Prachi Nandi, Sonakshi Satpathy, Timam Ghosh, and Arijit Roy that addresses privacy and efficiency challenges in IoT-based Federated Learning. The study proposes a greedy decentralized algorithm to select optimal client nodes by evaluating delay, energy consumption, and link reliability, aiming to enhance model convergence in multi-application environments.
Experimental results using a Human Activity Recognition dataset demonstrate that this greedy approach outperforms random selection and Q-learning strategies by achieving lower latency and improved energy efficiency. The authors highlight that while current FL-as-a-service (FLaaS) models face security and privacy hurdles, efficient client selection is critical for supporting intelligent services in Society 5.0. Future work suggests integrating data heterogeneity handling and differential privacy to further improve robustness and fairness.
The paper targets a key operational challenge in Federated Learning-as-a-Service (FLaaS): how to efficiently select participating clients when a federated learning platform must support multiple applications in a Society 5.0 IoT environment. In such settings, large numbers of heterogeneous devices generate highly personalized and privacy-sensitive data, making centralized training impractical. FLaaS offers a service-oriented alternative, but not all clients can or should be involved in every training round. The work focuses on client selection as the mechanism for balancing model quality, communication cost, device constraints, and application-specific requirements across a shared FL infrastructure.
Its main contribution is an application-aware client selection approach for multi-tenant or multi-application FLaaS deployments. Rather than treating clients as a homogeneous pool, the paper emphasizes that different applications may benefit from different subsets of devices depending on data relevance, model freshness, resource availability, and service-level objectives. By formulating selection around these factors, the approach aims to reduce unnecessary participation and network overhead while preserving the statistical representativeness needed for effective federated model updates. This is particularly important in multi-application scenarios, where competing workloads and diverse data distributions can degrade performance if clients are selected indiscriminately.
The work matters because it addresses the scalability gap between privacy-preserving distributed learning and practical service deployment in future IoT ecosystems. Efficient client selection is a prerequisite for making FLaaS viable at scale: it can lower training latency, conserve device energy and bandwidth, and improve the responsiveness of personalized services across domains such as smart cities, healthcare, and connected mobility. In that sense, the paper contributes to the broader goal of enabling Society 5.0 applications that are both data-driven and privacy-aware.