Harsh Shukla

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 devices. While clustering-based approaches mitigate statistical heterogeneity and pruning techniques improve memory and communication efficiency, these strategies are typically studied in isolation. We propose CA-AFP, a unified framework that jointly addresses both challenges by performing cluster-specific model pruning. In CA-AFP, clients are first grouped into clusters, and a separate model for each cluster is adaptively pruned during training. The framework introduces two key innovations: (1) a cluster-aware importance scoring mechanism that combines weight magnitude, intra-cluster coherence, and gradient consistency to identify parameters for pruning, and (2) an iterative pruning schedule that progressively removes parameters while enabling model self-healing through weight regrowth. We evaluate CA-AFP on two widely used human activity recognition benchmarks, UCI HAR and WISDM, under natural user-based federated partitions. Experimental results demonstrate that CA-AFP achieves a favorable balance between predictive accuracy, inter-client fairness, and communication efficiency. Compared to pruning-based baselines, CA-AFP consistently improves accuracy and lower performance disparity across clients with limited fine-tuning, while requiring substantially less communication than dense clustering-based methods. It also shows robustness to different Non-IID levels of data. Finally, ablation studies analyze the impact of clustering, pruning schedules and scoring mechanism offering practical insights into the design of efficient and adaptive FL systems.

Aditeya Pandey, Harsh Shukla, Geoffrey S. Young et al.

Blood circulation in the human brain is supplied through a network of cerebral arteries. If a clinician suspects a patient has a stroke or other cerebrovascular condition they order imaging tests. Neuroradiologists visually search the resulting scans for abnormalities. Their visual search tasks correspond to the abstract network analysis tasks of browsing and path following. To assist neuroradiologists in identifying cerebral artery abnormalities we designed CerebroVis, a novel abstract---yet spatially contextualized---cerebral artery network visualization. In this design study, we contribute a novel framing and definition of the cerebral artery system in terms of network theory and characterize neuroradiologist domain goals as abstract visualization and network analysis tasks. Through an iterative, user-centered design process we developed an abstract network layout technique which incorporates cerebral artery spatial context. The abstract visualization enables increased domain task performance over 3D geometry representations, while including spatial context helps preserve the user's mental map of the underlying geometry. We provide open source implementations of our network layout technique and prototype cerebral artery visualization tool. We demonstrate the robustness of our technique by successfully laying out 61 open source brain scans. We evaluate the effectiveness of our layout through a mixed methods study with three neuroradiologists. In a formative controlled experiment our study participants used CerebroVis and a conventional 3D visualization to examine real cerebral artery imaging data and to identify a simulated intracranial artery stenosis. Participants were more accurate at identifying stenoses using CerebroVis (absolute risk difference 13%). A free copy of this paper, the evaluation stimuli and data, and source code are available at https://osf.io/e5sxt/.

Dolton Fernandes, Pramod Kaushik, Harsh Shukla et al.

Traditional Reinforcement Learning (RL) algorithms assume the distribution of the data to be uniform or mostly uniform. However, this is not the case with most real-world applications like autonomous driving or in nature where animals roam. Some experiences are encountered frequently, and most of the remaining experiences occur rarely; the resulting distribution is called Zipfian. Taking inspiration from the theory of complementary learning systems, an architecture for learning from Zipfian distributions is proposed where important long tail trajectories are discovered in an unsupervised manner. The proposal comprises an episodic memory buffer containing a prioritised memory module to ensure important rare trajectories are kept longer to address the Zipfian problem, which needs credit assignment to happen in a sample efficient manner. The experiences are then reinstated from episodic memory and given weighted importance forming the trajectory to be executed. Notably, the proposed architecture is modular, can be incorporated in any RL architecture and yields improved performance in multiple Zipfian tasks over traditional architectures. Our method outperforms IMPALA by a significant margin on all three tasks and all three evaluation metrics (Zipfian, Uniform, and Rare Accuracy) and also gives improvements on most Atari environments that are considered challenging

Harsh Shukla

Steer-by-Wire (SBW) systems are being adapted widely in semi-autonomous and fully autonomous vehicles. The main control challenge in a SBW system is to follow the steering commands in the face of parametric uncertainties, external disturbances and actuator delay; crucially, perturbations in inertial parameters and damping forces give rise to state-dependent uncertainties, which cannot be bounded a priori by a constant. However, the state-of-the-art control methods of SBW system rely on a priori bounded uncertainties, and thus, become inapplicable when state-dependent dynamics become unknown. In addition, ensuring tracking accuracy under actuator delay is always a challenging task. This work proposes two control frameworks to overcome these challenges. Firstly, an adaptive controller is proposed to tackle the state-dependent uncertainties and external disturbances in a typical SBW system without any a priori knowledge of their structures and of their bounds. The stability of the closed-loop system is studied analytically via uniformly ultimately bounded notion and the effectiveness of the proposed solution is verified via simulations against the state-of-the-art solution. While this proposed controller handles the uncertainties and external perturbations, it does not consider the actuator delay which sometimes result in decreased accuracy. Therefore, a new adaptive-robust control framework is devised to tackle the same control problem of an SBW system under the influence of time-varying input delay. In comparison to the existing strategies, the proposed framework removes the conservative assumption of a priori bounded uncertainty and, in addition, the Razumikhin theorem based stability analysis allows the proposed scheme to deal with arbitrary variation in input delay. The effectiveness of the both controllers is proved using comparative simulation studies.