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Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning
Vedant Waykole, Haroon R. Lone
Federated learning on edge devices must cope with non-IID client data and tight memory budgets. Adaptive optimizers like Adam stabilize training under data heterogeneity but requir…
FedPrism: Adaptive Personalized Federated Learning under Non-IID Data
Prakash Kumbhakar, Shrey Srivastava, Haroon R Lone
Federated Learning (FL) suffers significant performance degradation in real-world deployments characterized by moderate to extreme statistical heterogeneity (non-IID client data).…
CA-AFP: Cluster-Aware Adaptive Federated Pruning
Om Govind Jha, Harsh Shukla, Haroon R. Lone
Federated Learning (FL) faces major challenges in real-world deployments due to statistical heterogeneity across clients and system heterogeneity arising from resource-constrained…
Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
Sharmad Kalpande, Saurabh Shirke, Haroon R. Lone
Mood instability is a key behavioral indicator of mental health, yet traditional assessments rely on infrequent and retrospective reports that fail to capture its continuous nature…
Fairness-Aware Few-Shot Learning for Audio-Visual Stress Detection
Anushka Sanjay Shelke, Aditya Sneh, Arya Adyasha +1
Fairness in AI-driven stress detection is critical for equitable mental healthcare, yet existing models frequently exhibit gender bias, particularly in data-scarce scenarios. To ad…
HCFSLN: Adaptive Hyperbolic Few-Shot Learning for Multimodal Anxiety Detection
Aditya Sneh, Nilesh Kumar Sahu, Anushka Sanjay Shelke +2
Anxiety disorders impact millions globally, yet traditional diagnosis relies on clinical interviews, while machine learning models struggle with overfitting due to limited data. La…