## 2 Related Work  
Client selection plays a central role in FL, as it determines which subset of devices contributes to each training round. While early works relied on random selection, recent research has moved toward intelligent and adaptive selection mechanisms that better reflect real-world constraints such as device heterogeneity, battery limitations, and privacy requirements. One of the first notable attempts to move beyond random selection is federated client selection (FedCS) [6], which selects clients based on their computational and communication capabilities. This method laid the foundation for system-aware FL by acknowledging that not all devices are equally capable. However, it primarily targets deadline compliance and does not adapt to dynamic battery states or consider user privacy. To incorporate data quality and responsiveness into the selection process, [PERSON] [7] introduced a utility-based client selection framework using reinforcement learning. Oort builds on the idea of system-awareness from FedCS but enhances it with real-time data-driven feedback. Nevertheless, it continues to neglect privacy constraints and does not explicitly model battery usage. Recognizing the need for fairness in participation, fair resource scheduling (FRS) [8] shifts the focus toward balanced resource allocation across heterogeneous clients. FRS takes a step further by promoting equitable use of devices over time. However, like its predecessors, it lacks mechanisms for real-time privacy control or battery-aware selection. To address personalization and user-side priorities, FedProm [9] proposes a preference and fairness aware selection scheme. While it introduces user centric customization, FedProm is still constrained by assumptions of uniform hardware profiles and does not adapt to battery or privacy dynamics during runtime. From a network-efficiency perspective, FedGCS [10] leverages dynamic graphs to optimize latency-aware client selection. It brings in network topology as a selection factor but assumes homogeneous device characteristics and overlooks both battery and privacy concerns. An alternative path is taken by multi device aggregation (MDA) [11], which enables several low-power devices to collaboratively contribute to model updates. This method improves inclusion and participation from battery constrained devices, but it does not enforce any safeguards on battery usage or sensitive data exposure. Another grouping-based method, tiered FL (TiFL) [12], clusters devices into static performance-based tiers to mitigate straggler effects. TiFL improves efficiency in device selection but is rigid and incapable of adapting to changing client contexts like battery status or data sensitivity. Beyond the discussed approaches, several recent studies have advanced client selection by integrating privacy preservation and battery efficiency with more granular adaptation. For instance, Ref. [13] proposes a privacy-preserving client selection scheme leveraging differential privacy to prevent sensitive information leakage during training, while [14] introduces an energy-aware scheduling algorithm that dynamically adjusts client participation to optimize device lifetime. Other works, such as [15], explore personalized FL client selection that considers both user preferences and device heterogeneity, addressing some limitations of static or hardware-homogeneous assumptions. These contributions underscore the multi-faceted nature of client selection challenges, yet they often treat energy, privacy, or personalization in isolation, without a unified framework balancing these critical factors simultaneously. Despite these advances, current methods either target individual aspects (e.g., accuracy, fairness, or latency) or assume homogeneity in client capabilities. They do not fully embrace the multi-objective, dynamic nature of real-world deployments.