Early-exit networks (EENs), which adapt their computational depths based on input samples, are widely adopted to accelerate inference in edge computing applications. The effectiveness of EENs relies on difficulty-aware training, which tailors shallow exits for simple samples and deep exits for complex ones. However, existing difficulty-aware training schemes assume centralized environments with sufficient data, which become invalid with real-world edge devices. In this paper, we explore difficulty-aware training in a federated manner, where EENs are collaboratively trained on heterogeneous devices. We observe the cross-model exit unalignment phenomenon, a unique problem when aggregating local EENs into a cohesive global model. To address this problem, we design a novel Difficulty-Aligned Reverse Knowledge Distillation scheme named DarkDistill that preserves the difficulty-specific specialization for aggregating heterogeneous local models. Instead of direct parameter averaging, it trains difficulty-conditional data generators, and selectively transfers generated knowledge of specific difficulty among matched exits of heterogeneous EENs. Evaluations show that DarkDistill outperforms the state-of-the-arts in both full-parameter and parameter-efficient fine-tuning of EENs.

Clustered Federated Learning (CFL) is an emerging paradigm to extract insights from data on IoT devices. Through iterative client clustering and model aggregation, CFL adeptly manages data heterogeneity, ensures privacy, and delivers personalized models to heterogeneous devices. Traditional CFL approaches, which operate synchronously, suffer from prolonged latency for waiting slow devices during clustering and aggregation. This paper advocates a shift to asynchronous CFL, allowing the server to process client updates as they arrive. This shift enhances training efficiency yet introduces complexities to the iterative training cycle. To this end, we present CASA, a novel CFL scheme for Clustering-Aggregation Synergy under Asynchrony. Built upon a holistic theoretical understanding of asynchrony's impact on CFL, CASA adopts a bi-level asynchronous aggregation method and a buffer-aided dynamic clustering strategy to harmonize between clustering and aggregation. Extensive evaluations on standard benchmarks show that CASA outperforms representative baselines in model accuracy and achieves 2.28-6.49× higher convergence speed.

Flight planning, a pivotal challenge in the airline industry, strives to achieve economic and flexible scheduling of airplanes to serve designated flight itineraries. As the demand for air transportation soars, traditional planning methods can be inefficient in managing large-scale flights. Thus, we introduce Swift, a data-driven system tailored to enhance the scalability and effectiveness of flight planning. Swift primarily employs the bipartite graph model to derive optimal and economic flight plans for airlines. Our method not only minimizes the number of required planes but also ensures a balanced workload across these planes. Furthermore, Swift offers the capability of dynamic updates to flight plans in response to unexpected incidents at airports, such as bad weather conditions. Besides, Swift incorporates other functionalities like predicting future flight demand and monitoring real-time flight trajectories. Conference participants can interact with this system and explore our flight planning solution in real-world scenarios.

Data federation has emerged as a novel database system enabling collaborative queries across mutually distrusted data owners. Federated equi-join, a commonly used operation in data federation, combines relations from distinct data owners while preserving their data privacy. Due to the wide applications of this query, many solutions to federated equi-joins have been proposed. However, it is still challenging for practitioners to choose the most appropriate algorithm due to various reasons, including incomplete evaluation protocols (e.g., lack of evaluating multi-way equi-joins), under-explored performance metric (main memory usage), and absence of a standardized comparison. Motivated by this reason, this paper conducts a comprehensive experimental study and builds a new benchmark, called${\sf FEJ-Bench}$, for federated equi-joins. The experimental study and the benchmark consist of eight state-of-the-art algorithms and five datasets. Our evaluation reveals the query efficiency ranking, its impact factors, and potential research opportunities. Finally, we open-source${\sf FEJ-Bench}$on GitHub, which is the first benchmark for federated equi-joins. Our findings aim to guide researchers and practitioners in deploying federated equi-joins in practice.

Federated Trajectory Matching (FTM) is gaining increasing importance in big trajectory data analytics, supporting diverse applications such as public health, law enforcement, and emergency response. FTM retrieves trajectories that match with a query trajectory from a large-scale trajectory database, while safeguarding the privacy of trajectories in both the query and the database. A naive solution to FTM is to process the query through Secure Multi-party Computation (SMC) across the entire database, which is inherently secure yet inevitably slow due to the massive secure operations. A promising acceleration strategy is to filter irrelevant trajectories from the database based on the query, thus reducing the SMC operations. However, a key challenge is how to publish the query in a way that both preserves privacy and enables efficient trajectory filtering. In this paper, we design${\sf GIST}$, a novel framework for efficient Federated Trajectory Matching.${\sf GIST}$is grounded in Geo-Indistinguishability, a privacy criterion dedicated to locations. It employs a new privacy mechanism for the query that facilitates efficient trajectory filtering. We theoretically prove the privacy guarantee of the mechanism and the accuracy of the filtering strategy of${\sf GIST}$. Extensive evaluations on five real datasets show that${\sf GIST}$is significantly faster and incurs up to 2 orders of magnitude lower communication cost than the state-of-the-arts.

Spatial crowdsourcing has stimulated various new applications such as taxi calling and food delivery. A key enabler for these spatial crowdsourcing based applications is to plan routes for crowd workers to execute tasks given diverse requirements of workers and the spatial crowdsourcing platform. Despite extensive studies on task planning in spatial crowdsourcing, few have accounted for the location privacy of tasks, which may be misused by an untrustworthy platform. In this paper, we explore efficient task planning for workers while protecting the locations of tasks. Specifically, we define the Privacy-Preserving Task Planning (PPTP) problem, which aims at both total revenue maximization of the platform and differential privacy of task locations. We first apply the Laplacian mechanism to protect location privacy, and analyze its impact on the total revenue. Then we propose an effective and efficient task planning algorithm for the PPTP problem. Extensive experiments on both synthetic and real datasets validate the advantages of our algorithm in terms of total revenue and time cost.