Cross-domain federated learning aims to collaboratively train a generalized model across clients with heterogeneous domain distributions without sharing data. Existing methods typically leverage prototypes to align intermediate representations among local models and enhance collaborative knowledge sharing, constructed either by directly aggregating class-center features across clients or by performing clustering to improve diversity. However, their performance is limited by the suboptimal ability to balance the learning of generalized and domain-specific features. To address this issue, this paper presents a novel dual-prototype guided FL framework named FedOrthrus, which decomposes the prototype into two components: i) the generalized prototype to capture cross-client domain-invariant features, and ii) the domain-specific prototype to extract the specific features of each domain. Specifically, the cloud server aggregates generalized prototypes to capture shared semantics across clients, thereby guiding each client to learn domain-invariant representations. Meanwhile, a clustering strategy is employed to adaptively construct domain-specific prototypes, ensuring that the representational capacity allocated to each domain is balanced according to its semantic complexity. Moreover, FedOrthrus employs a distribution-aware prototype construction scheme to dynamically assign the size of each part of prototypes, which enhances adaptability to different levels of domain heterogeneity. The experimental results on three datasets demonstrate that our FedOrthrus can achieve up to 14.56% and 3.96% accuracy improvement compared to traditional and state-of-the-art prototype-based FL methods. Our code is available at https://github.com/AAuZZ/FedOrthrus.

As a promising privacy-aware collaborative model training paradigm, Federated Learning (FL) is becoming popular in the design of distributed recommender systems. However, Federated Recommender Systems (FedRecs) greatly suffer from two major problems: i) extremely high communication overhead due to massive item embeddings involved in recommendation systems, and ii) intolerably low training efficiency caused by the entanglement of both heterogeneous network environments and client devices. Although existing methods attempt to employ various compression techniques to reduce communication overhead, due to the parameter errors introduced by model compression, they inevitably suffer from model performance degradation. To simultaneously address the above problems, this paper presents a communication-efficient FedRec framework named FedRAS, which adopts an action-sharing strategy to cluster the gradients of item embedding into a specific number of model updating actions for communication rather than directly compressing the item embeddings. In this way, the cloud server can use the limited actions from clients to update all the items. Since gradient values are significantly smaller than item embeddings, constraining the directions of gradients (i.e., the action space) introduces smaller errors compared to compressing the entire item embedding matrix into a reduced space. To accommodate heterogeneous devices and network environments, FedRAS incorporates an adaptive clustering mechanism that dynamically adjusts the number of actions. Comprehensive experiments on well-known datasets demonstrate that FedRAS can reduce the size of communication payloads by up to 96.88%, while not sacrificing recommendation performance within various heterogeneous scenarios. We have open-sourced FedRAS at https://github.com/mastlab-T3S/FedRAS.

As web applications grow in popularity, developers are increasingly integrating deep learning (DL) models into these environments. Web-based DL frameworks (e.g., TensorFlow.js) are essential for building and deploying such applications. Therefore, ensuring the quality of these frameworks is critical. While extensive testing efforts have been made for native DL frameworks such as TensorFlow and PyTorch, web-based DL frameworks have not yet undergone systematic testing. A key challenge is generating syntactically and semantically valid inputs while designing effective test oracles for web environments. To address this, we introduce TensorJSFuzz, a novel method for testing web-based DL frameworks. To ensure input quality, TensorJSFuzz extracts constraints directly from the source code of DL operators. By leveraging Large Language Models (e.g., ChatGPT) to understand the code and extract input constraints, TensorJSFuzz performs type-aware random generation coupled with dependency-aware refinement to create high-quality test inputs. These inputs are then subjected to differential testing across various backends, including CPU, TensorFlow, Wasm, and WebGL. Our experimental results show that TensorJSFuzz outperforms all baselines in generating valid inputs and identifying bugs. In particular, TensorJSFuzz successfully detected 92 bugs, with 30 already confirmed or fixed by developers, demonstrating its effectiveness in improving the robustness of web-based DL frameworks.

Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the un- evenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregat- ing local models usually results in a low-generalized global model, which performs worse on most of the clients. To address the above issue, inspired by the observation from a geometric perspective that a well-generalized solution is located in a flat area rather than a sharp area, we propose a novel and heuristic FL paradigm named FedMR (Federated Model Recombination). The goal of FedMR is to guide the recombined models to be trained towards a flat area. Unlike conventional FedAvg-based methods, in FedMR, the cloud server recombines collected local models by shuffling each layer of them to generate multiple recombined models for local training on clients rather than an aggregated global model. Since the area of the flat area is larger than the sharp area, when local models are located in different areas, recombined models have a higher probability of locating in a flat area. When all recombined models are located in the same flat area, they are optimized towards the same direction. We theoretically analyze the convergence of model recombination. Experimental results show that, compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without exposing the privacy of each client.

peer reviewed

Deep neural networks (DNNs) are vulnerable to adversarial examples which are generated by inputs with imperceptible perturbations. Understanding adversarial robustness of DNNs has become an important issue, which would for certain result in better practical deep learning applications. To address this issue, we try to explain adversarial robustness for deep models from a new perspective of critical attacking route, which is computed by a gradient-based influence propagation strategy. Similar to rumor spreading in social networks, we believe that adversarial noises are amplified and propagated through the critical attacking route. By exploiting neurons’ influences layer by layer, we compose the critical attacking route with neurons that make the highest contributions towards model decision. In this paper, we first draw the close connection between adversarial robustness and critical attacking route, as the route makes the most non-trivial contributions to model predictions in the adversarial setting. By constraining the propagation process and node behaviors on this route, we could weaken the noise propagation and improve model robustness. Also, we find that critical attacking neurons are useful to evaluate sample adversarial hardness that images with higher stimulus are easier to be perturbed into adversarial examples.