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Zhenyang Ni

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3 papers

1 author row

ICLR Conference 2024 Conference Paper

Fake It Till Make It: Federated Learning with Consensus-Oriented Generation

Rui Ye 0001
Yaxin Du
Zhenyang Ni
Yanfeng Wang 0001
Siheng Chen

In federated learning (FL), data heterogeneity is one key bottleneck that causes model divergence and limits performance. Addressing this, existing methods often regard data heterogeneity as an inherent property and propose to mitigate its adverse effects by correcting models. In this paper, we seek to break this inherent property by generating data to complement the original dataset to fundamentally mitigate heterogeneity level. As a novel attempt from the perspective of data, we propose federated learning with consensus-oriented generation (FedCOG). FedCOG consists of two key components at the client side: complementary data generation, which generates data extracted from the shared global model to complement the original dataset, and knowledge-distillation-based model training, which distills knowledge from global model to local model based on the generated data to mitigate over-fitting the original heterogeneous dataset. FedCOG has two critical advantages: 1) it can be a plug-and-play module to further improve the performance of most existing FL methods, and 2) it is naturally compatible with standard FL protocols such as Secure Aggregation since it makes no modification in communication process. Extensive experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods. Code is available at https://github.com/rui-ye/FedCOG.

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ICRA Conference 2024 Conference Paper

Robust Collaborative Perception without External Localization and Clock Devices

Zixing Lei
Zhenyang Ni
Ruize Han
Shuo Tang
Chen Feng 0002
Siheng Chen
Yanfeng Wang 0001

A consistent spatial-temporal coordination across multiple agents is fundamental for collaborative perception, which seeks to improve perception abilities through information exchange among agents. To achieve this spatial-temporal alignment, traditional methods depend on external devices to provide localization and clock signals. However, hardware-generated signals could be vulnerable to noise and potentially malicious attack, jeopardizing the precision of spatial-temporal alignment. Rather than relying on external hardwares, this work proposes a novel approach: aligning by recognizing the inherent geometric patterns within the perceptual data of various agents. Following this spirit, we propose a robust collaborative perception system that operates independently of external localization and clock devices. The key module of our system, FreeAlign, constructs a salient object graph for each agent based on its detected boxes and uses a graph neural network to identify common subgraphs between agents, leading to accurate relative pose and time. We validate FreeAlign on both real-world and simulated datasets. The results show that, the FreeAlign empowered robust collaborative perception system perform comparably to systems relying on precise localization and clock devices. ${\mathbf{Code}}$ will be released.

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ICML Conference 2023 Conference Paper

Personalized Federated Learning with Inferred Collaboration Graphs

Rui Ye 0001
Zhenyang Ni
Fangzhao Wu
Siheng Chen
Yanfeng Wang 0001

Personalized federated learning (FL) aims to collaboratively train a personalized model for each client. Previous methods do not adaptively determine who to collaborate at a fine-grained level, making them difficult to handle diverse data heterogeneity levels and those cases where malicious clients exist. To address this issue, our core idea is to learn a collaboration graph, which models the benefits from each pairwise collaboration and allocates appropriate collaboration strengths. Based on this, we propose a novel personalized FL algorithm, pFedGraph, which consists of two key modules: (1) inferring the collaboration graph based on pairwise model similarity and dataset size at server to promote fine-grained collaboration and (2) optimizing local model with the assistance of aggregated model at client to promote personalization. The advantage of pFedGraph is flexibly adaptive to diverse data heterogeneity levels and model poisoning attacks, as the proposed collaboration graph always pushes each client to collaborate more with similar and beneficial clients. Extensive experiments show that pFedGraph consistently outperforms the other $14$ baseline methods across various heterogeneity levels and multiple cases where malicious clients exist. Code will be available at https: //github. com/MediaBrain-SJTU/pFedGraph.

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