In Federated Learning (FL), multiple clients collaboratively train a model without sharing raw data. This paradigm can be further enhanced by Differential Privacy (DP) to protect local data from information inference attacks and is thus termed DPFL. An emerging privacy requirement, ``the right to be forgotten'' for clients, poses new challenges to DPFL but remains largely unexplored. Despite numerous studies on federated unlearning (FU), they are inapplicable to DPFL because the noise introduced by the DP mechanism compromises their effectiveness and efficiency. In this paper, we propose Federated Unlearning with Indistinguishability (FUI) to unlearn the local data of a target client in DPFL for the first time. FUI consists of two main steps: local model retraction and global noise calibration, resulting in an unlearning model that is statistically indistinguishable from the retrained model. Specifically, we demonstrate that the noise added in DPFL can endow the unlearning model with a certain level of indistinguishability after local model retraction, and then fortify the degree of unlearning through global noise calibration. Additionally, for the efficient and consistent implementation of the proposed FUI, we formulate a two-stage Stackelberg game to derive optimal unlearning strategies for both the server and the target client. Privacy and convergence analyses confirm theoretical guarantees, while experimental results based on four real-world datasets illustrate that our proposed FUI achieves superior model performance and higher efficiency compared to mainstream FU schemes. Simulation results further verify the optimality of the derived unlearning strategies.

Hubin Zhao (hubin.zhao@ucl.ac.uk), and Luigi G. Occhipinti (lgo23@cam.ac.uk) Abstract At-home rehabilitation for post-stroke patients presents significant challenges, as continuous, personalized care is often limited outside clinical settings. Additionally, the absence of comprehensive solutions addressing diverse rehabilitation needs in home environments complicates recovery efforts. Here, we introduce a smart home platform that integrates wearable sensors, ambient monitoring, and large language model (LLM)-powered assistance to provide seamless health monitoring and intelligent support. The system leverages machine learning enabled plantar pressure arrays for motor recovery assessment (94% classification accuracy), a wearable eye-tracking module for cognitive evaluation, and ambient sensors for precise smart home control (100% operational success, <1 s latency). Additionally, the LLM-powered agent, Auto-Care, offers real-time interventions, such as health reminders and environmental adjustments, enhancing user satisfaction by 29%. This work establishes a fully integrated platform for long-term, personalized rehabilitation, offering new possibilities for managing chronic conditions and supporting aging populations. Stroke is the third leading cause of disability worldwide, affecting more than 101 million people [1, 2]. Post-stroke recovery is not only a prolonged process but also a resource-intensive one, imposing significant economic and caregiving burdens on families and healthcare systems--a challenge exacerbated by global aging [5]. For many patients, the home becomes a critical environment for rehabilitation, as opportunities for continuous and personalized care are limited outside of clinical settings [6].

Parking challenges escalate significantly during large events such as concerts or sports games, yet few studies address dynamic parking lot assignments for such occasions. This paper introduces a smart navigation system designed to optimize parking assignments swiftly during large events, utilizing a mixed search algorithm that accounts for the heterogeneous characteristics of drivers. We conducted simulations in the Berkeley city area during the "Big Game" to validate our system and demonstrate the benefits of our innovative parking assignment approach.

This paper explores the optimization of Ground Delay Programs (GDP), a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports. Employing Reinforcement Learning (RL) to manage the inherent uncertainties in the national airspace system-such as weather variability, fluctuating flight demands, and airport arrival rates-we developed two RL models: Behavioral Cloning (BC) and Conservative Q-Learning (CQL). These models are designed to enhance GDP efficiency by utilizing a sophisticated reward function that integrates ground and airborne delays and terminal area congestion. We constructed a simulated single-airport environment, SAGDP_ENV, which incorporates real operational data along with predicted uncertainties to facilitate realistic decision-making scenarios. Utilizing the whole year 2019 data from Newark Liberty International Airport (EWR), our models aimed to preemptively set airport program rates. Despite thorough modeling and simulation, initial outcomes indicated that the models struggled to learn effectively, attributed potentially to oversimplified environmental assumptions. This paper discusses the challenges encountered, evaluates the models' performance against actual operational data, and outlines future directions to refine RL applications in ATM.

Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as enroute weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction.

Label noise is prevalent in machine learning datasets. It is crucial to identify and remove label noise because models trained on noisy data can have substantially reduced accuracy and generalizability. Most existing label noise detection approaches are designed for classification tasks, and data cleaning for outcome prediction analysis is relatively unexplored. Inspired by the fluctuations in performance across different folds in cross-validation, we propose Repeated Cross-Validations for label noise estimation (ReCoV) to address this gap. ReCoV constructs a noise histogram that ranks the noise level of samples based on a large number of cross-validations by recording sample IDs in each worst-performing fold. We further propose three approaches for identifying noisy samples based on noise histograms to address increasingly complex noise distributions. We show that ReCoV outperforms state-of-the-art algorithms for label cleaning in a classification task benchmark. More importantly, we show that removing ReCoV-identified noisy samples in two medical imaging outcome prediction datasets significantly improves model performance on test sets. As a statistical approach that does not rely on hyperparameters, noise distributions, or model structures, ReCoV is compatible with any machine learning analysis.

Earlier-stage evaluations of a new AI architecture/system need affordable AI benchmarks, while using a few AI component benchmarks alone in the other stages may lead to misleading conclusions. This paper proposes a balanced benchmarking methodology. Performing an exhaustive survey on Internet service AI domains, we identify and implement seventeen representative AI tasks with the state-of-the-art models to guarantee the diversity and representativeness of the benchmarks. Meanwhile, we keep a benchmark subset to a minimum for affordability. We contribute by far the most comprehensive AI training benchmark suite with seventeen industry partners. The evaluations show: (1) AIBench Training outperforms MLPerf Training in terms of the diversity and representativeness of model complexity, computational cost, convergent rate, computation and memory access patterns, and hotspot functions; (2) With respect to the AIBench full benchmarks, its subset shortens the benchmarking cost by 54%, while maintaining the primary workload characteristics; (3) The performance ranking shows the single-purpose AI accelerator like TPU with the optimized TensorFlow framework performs better than that of GPUs while losing the latters' general support for a variety of AI models. The AIBench Training specifications, source code, testbed, and performance numbers are publicly available from the web site http://www.benchcouncil.org/AIBench/index.html.