Federated learning (FL) has achieved great success as a privacy-preserving distributed training paradigm, where many edge devices collaboratively train a machine learning model by sharing the model updates instead of the raw data with a server. However, the heterogeneous computational and communication resources of edge devices give rise to stragglers that significantly decelerate the training process. To mitigate this issue, we propose a novel FL framework named stochastic coded federated learning (SCFL) that leverages coded computing techniques. In SCFL, before the training process starts, each edge device uploads a privacy-preserving coded dataset to the server, which is generated by adding Gaussian noise to the projected local dataset. During training, the server computes gradients on the global coded dataset to compensate for the missing model updates of the straggling devices. We design a gradient aggregation scheme to ensure that the aggregated model update is an unbiased estimate of the desired global update. Moreover, this aggregation scheme enables periodical model averaging to improve the training efficiency. We characterize the tradeoff between the convergence performance and privacy guarantee of SCFL. In particular, a more noisy coded dataset provides stronger privacy protection for edge devices but results in learning performance degradation. We further develop a contract-based incentive mechanism to coordinate such a conflict. The simulation results show that SCFL learns a better model within the given time and achieves a better privacy-performance tradeoff than the baseline methods. In addition, the proposed incentive mechanism grants better training performance than the conventional Stackelberg game approach.

我们考虑了一个联合表示的学习框架，在中央服务器的协助下，一组$ n $分布式客户通过其私人数据协作培训一组实体的表示（或嵌入）（例如，用户在一个中的用户社交网络）。在此框架下，对于以私人方式汇总在客户培训的本地嵌入的关键步骤，我们开发了一个名为SECEA的安全嵌入聚合协议，该协议为一组实体提供信息理论隐私保证，并在每个客户端提供相应的嵌入$同时$ $，对好奇的服务器和最多$ t <n/2 $勾结的客户。作为SECEA的第一步，联合学习系统执行了一个私人实体联盟，让每个客户在不知道哪个实体属于哪个客户的情况下学习系统中的所有实体。在每个聚合回合中，使用Lagrange插值在客户端中秘密共享本地嵌入，然后每个客户端构造编码的查询以检索预期实体的聚合嵌入。我们对各种表示的学习任务进行全面的实验，以评估SECEA的效用和效率，并从经验上证明，与没有（或具有较弱的）隐私保证的嵌入聚合协议相比，SECEA会造成可忽略的绩效损失（5％以内）； SECEA的附加计算潜伏期减小，用于培训较大数据集的更深层次模型。

我们提出了Swiftagg+，这是一种针对联合学习系统的新颖的安全聚合协议，其中central Server汇总了$ n \ in \ mathbb {n} $分布式用户的本地型号，每个大小$ l \ in \ mathbb {n} $中的每个型号，训练有素，以隐私的方式在其本地数据上。 Swiftagg+可以大大减少通信开销，而不会对安全性进行任何妥协，并在减少差距内实现最佳通信负载。具体而言，最多有$ d = o（n）$ droput用户，Swiftagg+实现了$（1+ \ Mathcal {o}（\ frac {1} {n} {n}））的每个用户通信负载。和$（1+ \ Mathcal {o}（\ frac {1} {n}））的服务器通信负载，具有最差的信息理论安全保证o（n）$半honest用户，也可能与好奇的服务器合谋。此外，拟议的Swiftagg+允许在通信负载和主动通信链接的数量之间进行灵活的权衡。特别是，对于$ t <n-d $，对于任何$ k \ in \ mathbb {n} $，Swiftagg+可以实现$（1+ \ frac {t} {k} {k}）l $符号的服务器通信负载，并且 - 用户通信负载最多$（1+ \ frac {t+d} {k}）l $符号，其中网络中的配对活动连接的数量为$ \ frac {n} {2}（k +T+D+1）$。

Federated学习（FL）作为保护分布式机器学习框架引起了很多关注，许多客户通过将模型更新与参数服务器交换而不是共享其原始数据来协作训练机器学习模型。然而，FL培训遭受了缓慢的收敛性和不稳定的性能，这是由于客户的异质计算资源引起的散乱者和沟通率的波动。本文提出了一个编码的FL框架来减轻Straggler问题，即随机编码的联合学习（SCFL）。在此框架中，每个客户端通过将附加噪声添加到其本地数据的随机线性组合中，从而生成一个隐私的编码数据集。服务器从所有客户端收集编码的数据集来构建复合数据集，这有助于补偿散布效果。在培训过程中，服务器和客户端执行迷你批次随机梯度下降（SGD），并且服务器在模型聚合中添加了一个化妆术语，以获得无偏的梯度估计。我们通过共同信息差异隐私（MI-DP）来表征隐私保证，并分析联合学习中的收敛性能。此外，我们通过分析隐私约束对收敛率的影响，证明了拟议的SCFL方法的隐私性绩效权衡。最后，数值实验证实了我们的分析，并显示了SCFL在保持数据隐私的同时实现快速收敛的好处。

我们提出了一个用于机器学习应用的基于区块链的安全数据交易市场的Omnilytics。利用omnilytics，许多分布式数据所有者可以贡献他们的私人数据，以集体培训某些型号所有者请求的ML模型，并获得数据贡献的补偿。 Omnilytics使这种模型培训能够同时为奇怪的数据所有者提供1）模型安全; 2）对奇怪的模型和数据所有者的数据安全; 3）对恶意数据所有者的弹性，为毒药模型培训提供有错误的结果; 4）打算逃避付款的恶意模型所有者的弹性。 Omnilytics被实施为一个区块链智能合同，以保证付款的原子。在omnilytics中，模型所有者将其模型分成私人和公共部分，并在合同上发布公共部分。通过执行合同，参与的数据所有者将其当地培训的模型安全地汇总以更新模型所有者的公共模式，并通过合同获得报销。我们在以Ethereum区块链中实施了Omnilytics的工作原型，并在各种参数组合下进行了广泛的实验，以测量其天然气成本，执行时间和模型质量。为了在MNIST数据集上训练CNN，MO能够将其模型精度从平板ChangchConsion Time的500毫秒内的62％提升到83％。这证明了Omnilytics对实际部署的有效性。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.