Federated learning (FL) involves multiple heterogeneous clients collaboratively training a global model via iterative local updates and model fusion.The generalization of FL's global model has a large gap compared with centralized training, which is its bottleneck for broader applications.In this paper, we study and improve FL's generalization through a fundamental "connectivity" perspective, which means how the local models are connected in the parameter region and fused into a generalized global model.The term "connectivity" is derived from linear mode connectivity (LMC), studying the interpolated loss landscape of two different solutions (e.g., modes) of neural networks.Bridging the gap between LMC and FL, in this paper, we leverage fixed anchor models to empirically and theoretically study the transitivity property of connectivity from two models (LMC) to a group of models (model fusion in FL).Based on the findings, we propose FedGuCci(+), improving group connectivity for better generalization.It is shown that our methods can boost the generalization of FL under client heterogeneity across various tasks (4 CV datasets and 6 NLP datasets) and model architectures (e.g., ViTs and PLMs).The code is available here: FedGuCci Codebase.

Large-scale vision-language (V-L) models have demonstrated remarkable generalization capabilities for downstream tasks through prompt tuning. However, the mechanisms behind the learned text representations are unknown, limiting further generalization gains, and the limitations are more severe when faced with the prevalent class imbalances seen in web-sourced datasets. Recent advances in the neural collapse (NC) phenomenon of vision-only models suggest that the optimal representation structure is the simplex ETF, which paves the way to study representations in V-L models. In this paper, we make the first attempt to use NC for examining the representations in V-L models via prompt tuning. It is found that NC optimality of text-to-image representations shows a positive correlation with downstream generalizability, which is more severe under class imbalance settings. To improve the representations, we propose Neural-collapse-anchored Prompt Tuning (NPT), a novel method that learns prompts with text and image representations that satisfy the same simplex Equiangular Tight Frame (ETF). NPT incorporates two regularization terms: language-modality collapse and multi-modality isomorphism; and it is compatible with other prompt tuning methods. Extensive experiments show that NPT can consistently help to improve existing prompt tuning techniques across 11 datasets for both balanced and imbalanced settings.

Federated learning (FL) is a popular way of edge computing that does not compromise user's privacy. Current FL paradigms assume data only resides on the edge, while cloud servers only perform model averaging. However, in real-life situations such as recommender systems, the cloud server usually has abundant features and computation resources. Specifically, the cloud stores historical and interactive features, and the edge stores privacy-sensitive and real-time features. In this paper, our proposed Edge-Cloud Collaborative Knowledge Transfer Framework (ECCT) jointly utilizes the edge-side features and the cloud-side features, enabling bi-directional knowledge transfer between the two by sharing feature embeddings and prediction logits. ECCT consolidates various benefits, including enhancing personalization, enabling model heterogeneity, tolerating training asynchronization, and relieving communication burdens. Extensive experiments on public and industrial datasets demonstrate the effectiveness of ECCT.

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.

Collaborative multi-agent reinforcement learning (MARL) has been widely used in many practical applications, where each agent makes a decision based on its own observation. Most mainstream methods treat each local observation as an entirety when modeling the decentralized local utility functions. However, they ignore the fact that local observation information can be further divided into several entities, and only part of the entities is helpful to model inference. Moreover, the importance of different entities may change over time. To improve the performance of decentralized policies, the attention mechanism is used to capture features of local information. Nevertheless, existing attention models rely on dense fully connected graphs and cannot better perceive important states. To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations. The local utility functions are estimated through the self-attention and sparse attention mechanisms separately, then are combined into a standard joint value function and auxiliary joint value function in the central critic. We design the S2RL framework as a plug-and-play module, making it general enough to be applied to various methods. Extensive experiments on StarCraft II show that S2RL can significantly improve the performance of many state-of-the-art methods.

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework.

The drive toward smart cities alongside the rising adoption of personal sensors is leading to a torrent of sensor data. While systems exist for storing and managing sensor data, the real value of such data is the insight which can be generated from it. However there is currently no platform which enables sensor data to be taken from collection, through use in models to produce useful data products. The architecture of such a platform is a current research question in the field of Big Data and Smart Cities. In this paper we explore five key challenges in this field and provide a response through a sensor data platform “Concinnity” which can take sensor data from collection to final product via a data repository and workflow system. This will enable rapid development of applications built on sensor data using data fusion and the integration and composition of models to form novel workflows. We summarize the key features of our approach, exploring how it enables value to be derived from sensor data efficiently.

Personal data collection is becoming pervasive these days, these data has the risk of being abused by current application and application marketplace model, because only the price of application is explicitly indicated without clear agreement on usage of data, and the granularity of data access authentication is not enough to protect users privacy. In this short paper, we propose a new model of user data privacy. Data usage of the application is explicitly shown, and controlled by an authentication service, to protect users from the abuse of their data, especially in mobile application.