Federated Graph Learning for Cross-Domain Recommendation

Authors: ziqi  yang
zhaopeng  peng
zihui  wang
jianzhong  qi
chaochao  chen
weike  pan
chenglu  wen
cheng  wang
xiaoliang  fan
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-08 04:49:11

Abstract: Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods.

Main paper: 
 introduction: 
 Cross-domain recommendation (CDR) has emerged as an effective solution for mitigating data sparsity in recommender systems [1, 2, 3, 4, 5] . CDR operates by integrating auxiliary information from source domains, thereby enhancing recommendation relevance in the target domain. Recently, to address data privacy constrains, many privacy-preserving CDR frameworks have been proposed [6, 7, 8, 9] , which achieve strong performance under the assumptions of data sparsity and a dual-domain model (i.e., typically involving a single source domain and a single target domain.).In this paper, we focus on a more generic scenario of Broader-Source Cross-Domain Recommendation (BS-CDR), which integrates knowledge from more than two source domains while (a) The BS-CDR scenario.(b) The performance affected by the number of domains Figure 1: (a) In order to obtain accurate recommendations in the Books domain, we aim to exploit user preferences (i.e., knowledge of external domains should be fully utilized, e.g. Movie, Toys, and Games domains). However, with the influence of lossy privacy-preserving techniques, the results of the transfer could be negative (e.g., the Music domain with low-quality data). (b) There is a diminishing marginal effect on the growth rate of the model performance with pure positive knowledge, while NT accumulates with an increasing number of source domains. Consequently, the performance of existing methods declines and is worse than that of a single domain model. preserving privacy. Given the diverse nature of user preferences, it is essential to gain a more holistic understanding of user interests by incorporating user behaviors from diversified domains [10, 11] . For example, in Figure 1a , a user who enjoys certain types of books might also enjoy movies, toys, and games in similar genres. However, incorporating more domains while preserving privacy poses challenges to counteract negative transfer (NT), which is a phenomenon of transferring knowledge from a source domain that negatively impacts the recommender model performance in the target domain [12] . Suppose the Books domain in Figure 1a is the target domain. The Clothing domain is causing NT, because of the domain discrepancy. While the Music domain is supposed to transfer positive knowledge, it might also lead to NT because of lossy privacy-preserving techniques applied to broader source domains. As a result, the influx of negative knowledge accumulated from source domains will poison the model performance of the target domain in BS-CDR scenarios.To mitigate the NT issue, attention mechanisms have been widely leveraged, either in an explicit (e.g., determine domain attentions by predefined domain features [13, 14] ) or implicit manner (e.g., employ hyper-parameters [7, 15] ). Several other studies [16, 17, 18] , ensure positive transfer by passing only domain-shared features. However, existing methods cannot be directly applied to BS-CDR due to two major challenges. First, inadequate privacy preservation (CH1). Both intra-domain and inter-domain privacy must be carefully considered in BS-CDR. As depicted in Figure 1a , BS-CDR relies on extensive knowledge transfer, risking simultaneous privacy leakages across broader source domains (inter-domain privacy) [9, 19, 20, 21] . Additionally, concerns over centralized data storage may prevent users from sharing sensitive rating data (intra-domain privacy). Second, accumulative negative transfer (CH2). Adjusting attention-related hyper-parameters for a large number of source domains in BS-CDR scenarios is extremely difficult, as well as predefined or domain-shared features cannot accommodate complex domain diversities. In addition, the use of various lossy privacypreserving techniques can further degrade the quality of transferred knowledge, complicating the achievement of positive transfer. Consequently, the impact of NT can inevitably intensify with an increasing number of source domains [2] and the performance of CDR models can decline to levels lower than those of single-domain models, as shown in Figure 1b .To address the challenges of privacy (CH1) and NT (CH2) in BS-CDR, we propose Federated Graph learning for Cross-Domain Recommendation (FedGCDR). It follows a horizontal-verticalhorizontal pipeline [6] and consists of two key modules. First, the positive knowledge transfer module aims to safeguard inter-domain privacy and mitigate potential NT before transfer. This module adopts differential privacy (DP) [22] with a theoretical guarantee and aligns the feature spaces to facilitate positive knowledge transfer. Second, the positive knowledge activation module is engaged to further alleviate NT. Specifically, it expands the local graph of the target domain by incorporating virtual social links, enabling the generation of domain attentions. Additionally, it performs target model fine-tuning to optimize the broader-source CDR. Extensive experiments on 16 popular domains from the Amazon benchmarks demonstrate that FedGCDR outperforms all baseline methods in terms of recommendation accuracy.Our contributions are summarized as follows:• We introduce FedGCDR, a novel federated graph learning framework for CDR that provides high-quality BS-CDR recommendations while safeguarding both user privacy and domain confidentiality;• We propose two key model, i.e., the positive knowledge transfer module and the positive knowledge activation module. The first transfer module ensure privacy and positive knowledge flows via privacy-preserving knowledge extraction and feature mapping. The second activation module filter harmful information via graph expansion, target domain training and target model fine-tuning;• We conduct extensive experiments on 16 domains of the Amazon datasets that confirm the effectiveness of FedGCDR in terms of recommendation accuracy.2 Related workQuestion: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?Answer: [Yes] Justification: We have claimed the contributions and scope in lines 5-7 and 69-75.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 
cross-domain recommendation: 
 CDR utilizes auxiliary information from external domains to alleviate the data sparsity problem and effectively improve recommendation quality. Li et al. [23] enrich domain knowledge by transferring user-item rating patterns from source domains to target domains. Man et al. [15] and Elkahky et al. [24] augment entities' embeddings in the target domain by employing a linear or multilayer perceptron (MLP)-based nonlinear mapping function across domains. Liu et al. [25] address the review-based non-overlapped recommendation problem by attribution alignment. Zhao et al. [18] improve the recommendation quality of multi-sparse-domains by mining domain-invariant preferences. Liu et al. [26] achieve knowledge transfer without overlapping users by mining joint preferences. Chen et al. [19] and Liu et al. [27] avoid intermediate result privacy leakage during cross-domain knowledge transfer by employing DP. In these works, the NT problem is often ignored because most of them assume a carefully selected dual-domain scenarios or limited multi-domain scenarios where NT is not evident. We aim to solve the NT problem in complex BS-CDR scenarios.Recently, FL [28, 29, 30, 31, 32] has been widely adopted to tackle the privacy issue in recommender system. Chai et al. [33] adopts FL to classic matrix factorization algorithm and utilize homomorphic encryption to avoid the potential threat of privacy disclosure. Later, Wu et al. [34] explores the application of federated graph neural networks (GNN) models to improve the recommendation quality and ensure user privacy. To utilize sensitive social information, Liu et al. [8] adopts local differential privacy (LDP) and negative sampling. More recent studies use VFL to protect company's privacy in recommender system. Mai et al. [35] utilizes random projection and ternary quantization to ensure privacy preservation in VFL. In CDR, Chen et al. [9] designs a dual-target VFL CDR model with orthogonal mapping matrix and LDP for organizations' privacy preservation. Liu et al. [36] designs a graph convolutional networks (GCN)-based federated framework to learn user preference distributions for more accurate recommendations. To ensure user privacy in CDR, Liu et al. [6] utilizes a VAE-based federated model to mine user preference with data stored locally. Wu et al. [7] designs a personal module and a transfer module to provide personalized recommendation while preserving user privacy. These existing works, especially federated CDR frameworks, consider only one type of privacy (intra-or inter-domain). We aim to provide both intra-domain and inter-domain privacy. Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes]Justification: We have discussed the limitations in lines 318-325.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
problem definition: 
 We consider M (M >3) domains participating in the CDR process. The domains are divided into M-1 source domains D S1 , D S2 , ..., D S M -1 and one target domain D T . Each domain is assigned a domain server to conduct intra-domain model training. U is the user set across all the domains, U = U 1 U 2 ... U M , where U i denotes the user set of domain i. We assume that users partially overlap between domains. Each user is treated as an individual client. User space refers to the virtual space in the user's device containing domain models distributed from each domain server. Meanwhile, V i is the item set of domain i. Let R i ∈ R |Ui|×|Vi| be the observed rating matrix of the i-th domain. We consider top-K recommendation, i.e., we learn a function to estimate the scores of unobserved entries in the rating matrix, which are later used for item ranking and recommendations. Our goal is to achieve highly accurate recommendations in the target domain.The overall framework of FedGCDR is shown in Figure 2 . FedGCDR follows a Horizontal-Vertical-Horizontal (HVH) pipeline and its two horizontal FL stages ensure the intra-domain privacy privacy. Our two key modules focus on the vertical stage and the second horizontal stage: (1) The positive knowledge transfer module preserves the inter-domain privacy by DP and alleviates NT by feature mapping. (2) The positive knowledge activation module filters out potential harmful or conflicting knowledge from the source domains. Specifically, we expand the local graph of the target domain by virtual social links, such that the target domain graph attention network (GAT) model could generate reliable domain attention based on the expanded graph. After target domain GAT model training, we further mitigate NT by adopting a fine-tuning stage.Horizontal-Vertical-Horizontal pipeline The HVH pipeline contains three stages with switching federated settings. The first horizontal stage refers to the source domain training in which source domain servers individually interacts with its domain users (clients). The private rating information is stored within each client, while the clients exchange model and gradients to train a domain-specific global model. The next two stages correspond to our two key modules (vertical positive knowledge transfer module and horizontal positive knowledge activation module), which we will cover in detail in the following subsections. It's important to note that the vertical positive knowledge transfer module is completely computed in each client's user space (their personal devices), thus reducing communication overheads. This is because the needed source domain knowledge can be extracted from local source models on each client which are distributed during the first horizontal source domain training stage.Following the HVH pipeline, we achieve: (1) Privacy enhancement. The two horizontal stages can provide intra-domain privacy preservation, while we further ensure inter-domain privacy by applying DP to the vertical stage. In the mean time, servers are not involved in the knowledge transfer process (i.e., the positive knowledge transfer module), making them unaware of user interactions in other source domains. (2) Communication efficiency. Cross-domain knowledge transfer does not require additional communication overhead.We adopt a GAT-based [37, 38, 39 ] federated framework as the underlying model for our intra-domain recommender system. The horizontal paradigm avoids centralized storage of user ratings to ensure intra-domain privacy (CH1). In the initial step, each user and item is offered an ID embedding of size d, denoted by e 0 u , e 0 v ∈ R d respectively. The embedding is passed through L message propagation layers [40, 41, 42] . For the l-th layer:EQUATIONwhere N u is the neighbor set of u, W l is a learnable weight matrix, a l uu and a l uv are the importance coefficients computed by the attention mechanism:EQUATION)where α is the weight vector. Inspired by LightGCN [43] , we discard feature transformation and nonlinear activation for better model efficiency and learning effectiveness:EQUATIONEQUATIONIn each source domain, the domain server and corresponding users collaboratively train a GAT-based federated model. The training process follows the horizontal federated learning (HFL) paradigm in which only the model and gradients are exchanged considering intra-domain privacy. We will not detail the horizontal federation model (e.g., further privacy guarantee and more high-order information) as it is a well established FL model and not our novel contribution. This model can be replaced by other GAT-based FL models [34, 44] as well.After the source domain training, we obtain a series of source models in individual client's user space.Our positive knowledge transfer module then prepares positive knowledge to be transferred from each source domains D S to the target domain D T , while protecting inter-domain privacy (CH1). Specifically, suppose a individual user (client) u and a source domain D Si , we transfer the user u's embedding matrix X Si ∈ R L×d . Take the row l of the matrix (i.e., x l Si ) as an example, it is the user u's embedding output by the l-th message propagation layer (e l u ). In an ideal scenario (i.e., we transfer totally positive knowledge without taking inter-domain privacy into account) [6] , embedding matrices from different source domains can be directly used to enhance target domain local training in client u. By utilizing the source domain embeddings, u's final target domain embedding e l T of layer l is:EQUATIONwhere f T (•) is the function that the target domain aggregates the knowledge of the source domains and we will give its final expression in Subsection 3.2.3. In this process, the transfer of knowledge between domains takes place entirely in the user u's local space. Such a fully localized mode of knowledge transfer avoiding the additional communication overhead and potential privacy issues [6] . However, this direct embeddings transfer does not meet the privacy and NT constrains in BS-CDR scenarios.In existing CDR frameworks, the user or item embedding was shared as knowledge [9, 15, 6] , which neglects inter-domain privacy. In a GNN-based approach, such direct transfers are subject to privacy attacks. Each message propagation layer can be viewed as a function with user and item embeddings as input. An attacker can easily obtain the user's private rating matrix based on these embeddings. We apply DP to the source domain embeddingsx Si [22, 45] to safeguard inter-domain privacy.THEOREM 1. By perturbing the source domain embeddings with Gaussian noise, the reconstructed data of the ideal attack deviates from the real data and prevents a perfect reconstruction.In FedGCDR, we adopt the Gaussian mechanism to the source domain embedding x Si to obtain xSi for knowledge transfer. Detailed privacy analysis is included in Appendix A.Feature mapping User features could represent personal preferences and are influenced by domain features. The discrepancy of domains leads to the heterogeneity of feature space between domains which means that source domain embeddings cannot be utilized directly by the target domain. Man et al. [15] show that there exists an underlying mapping relationship between the latent user matrix of different domains, which can be captured by a mapping function. In order to alleviate NT, we adopt a series of MLP to explore mapping functions for each source domain. Adding Gaussian noise and feature mapping, Equation ( 5) becomes:EQUATIONTo learn more effective mapping function, we adopt a mapping loss term:EQUATIONAfter the aforementioned operations, the target domain obtains a list of source domain matrices XS1 , XS2 , ..., XS M -1 . The row of the matrices represent M LP i (x l Si ). It is worth noting that for source domains where a user has no rating, XSi is a Gaussian noise matrix and our motivation is: (1) no rating may also suggest a preference; (2) this is beneficial for enhancing the model's capability to filter noise and identify NT. With the knowledge from the source domains, the positive knowledge activation module is to alleviate NT after the knowledge transfer (CH2). Although we have aligned the feature space in the previous module, the Gaussian noise that has been fed to the target domain with source domain embedding matrices leads to potential NT. How to utilize the transferred knowledge remains a great challenge. To alleviate NT, common approaches are to generate domain attention by predefined domain features [13, 46, 47] or to control the transfer ratio of source domains by hyper-parameters [7] . These methods are only applicable to a limited number of domains and have excessive human intervention. In FedGCDR, we take an attention-based approach. First, we expand u's (Mary's) local graph of the target domain as shown in Figure 3 .For the source domain embedding matrices XS1 , XS2 , ..., XS M -1 , we represent them as M -1 virtual users.Since the virtual users constructed from source domain embeddings represent the same individual u, they share correlated preferences, with their features (i.e., embeddings) characterizing u's preferences. Inspired by social recommendation [48, 49, 50] , we consider that there is a implicit social relationship between virtual users and the actual user u, because of the correlation in their preferences. Then, we build virtual social links between them to expand the original target domain graph. Second, by incorporating this expanded graph into target domain training, the GAT model generates corresponding attention coefficients for the virtual users, which can be interpreted as domain-specific attentions. Leveraging the domain attention coefficients, the target domain can focus on domains that transfer positive knowledge and we can finally give f T (•):EQUATIONwhere a l i is the domain attention of source domain i generated by the l-th layer. Beside, we introduce a social regularization term to strengthen the virtual social links:EQUATIONSince we do not have direct access to the rating matrix of each source domain, the function Sim(•) calculates the cosine similarity [48] .Through the graph expansion, we achieve: (1) dynamic domain attentions that focus on positive source domain knowledge to alleviate NT; (2) attention generation by GAT, eliminating the need for additional interventions such as hyper-parameter tuning or feature engineering.For top-k recommendation, we adopt a widely-used inner product model to estimate the value of target domain rating R T uv , which is the interaction probability between a pair of user u and item v:EQUATIONwhere e u T and e v T are the final user and item embeddings output by GAT. Our objective function consists of three terms as follows:EQUATIONwhere α and β are hyper-parameters, and BCELoss(•) is the binary cross-entropy loss [51] . The target domain federated GAT training with the expanded graph following the HFL paradigm.Target model fine-tuning After target domain training with the expanded graph, the target domain GAT model assimilates knowledge from the source domains. However, NT may still be unavoidable, potentially leading to the accumulation of negative knowledge in the target domain. An example of this is the Gaussian noise matrices transferred from source domains where the user has no interactions. On the basis of this consideration, we adopt an additional fine-tuning stage: First, we freeze the message propagation layer of GAT to isolate the influence of source domains preventing Gaussian noise from permeating through the transfer process. Second, we directly train the well-informed embeddings generated by the target domain GAT. These steps adapt the learned external knowledge for predicting the target domain ratings. In this process, we use the loss of prediction in Equation (11) as the object function:EQUATIONWe provide a computational analysis and a communication analysis of FedGCDR in Appendix B.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer: [Yes]Answer: [Yes] Justification: We provide the code in supplemental material. For datasets, we provide the data processing code and the public benchmark is easy to access via the link in code file. Guidelines: Guidelines:• The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.• If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction). 
experimental setup: 
 Datasets We study the effectiveness of FedGCDR with 16 popular domains on a real-world dataset Amazon [52] . To study the impact of the number of domains on model performance, we divide these domains into three subsets containing 4, 8, and 16 domains respectively and denote them as Amazon-4, Amazon-8, and Amazon-16 respectively. The statistics of sub-datasets are shown in Table 1 . We filter the original data in different ways, and more details are given in Appendix C.1. In our experiments, Books and CDs are selected as the target domains. For the ratings in each domain, we first convert them to implicit data, where entries corresponding to existing user-item interactions are marked as 1 and others are marked as 0.Baselines We compare FedGCDR with the following state-of-the-art models: (1) FedGNN [34] is an attempt to adopt FL graph learning to recommender systems. Its recommendation performance could represent the data quality of the target domain and reflect negative transfer. In Tables 2 and 3 , in order to distinguish FedGNN from the CDR baselines, we denote it by Single Domain.(2) EMCDR [15] is a conventional embedding-mapping CDR framework. We adjust it to the HFL framework following [6] . (3) PriCDR [19] is a privacy-preserving CDR framework, which adopted DP on the rating matrices rating matrix to ensure privacy. ( 4) FedCT [6] is a VAE-based federated framework that is the first attempt to protect intra-domain privacy in cross-domain recommendations.(5) FedCDR [7] is a dual-target federated CDR framework, where the user embeddings are transferred as knowledge to enhance the other domain's model training. To adapt to the BS-CDR scenarios, we modify FedCDR by applying embedding averaging when receiving source domain embeddings.We provide implement details in Appendix C.2.We report the model performance results in Tables 2 and 3 . Single domain shows that the Book domain has better single-domain recommendation accuracy than the Music domain, which represents higher data quality and quantity. Under BS-CDR settings, FedGCDR outperforms all CDR baselines on all three sub-datasets, which confirms the effectiveness of the proposed model on real-world data.To further study our model capacity in alleviating negative transfer, we first define two types of negative transfer: (1) Soft Negative Transfer (SNT), where recommender models' performance under the multi-domain setting is worse than that under the single-domain setting. This means that the knowledge from source domains poisoning the target domain's model training.(2) Hard Negative Transfer (HNT), where recommended performance of a large number of source domains is lower than that of a small number of source domains. This means that the newly added domains are not conducive to the training of the target domain or conflict with the already added source domain.Taking the Books domain as the target domain, EMCDR, PriCDR, FedCT and FedCDR both have serious negative transfer problems and lower performance on the three data subsets. From the SNT perspective, their performances is much worse than that of Single Domain as shown in Figure 4 .From the HNT perspective, their performances under 16-domain settings is worse than that under the 8-domain and 4-domain settings, which suggests it is not appropriate to recklessly transfer knowledge to a well-informed domain. Our FedGCDR model successfully alleviates NT with consistently best and stable performance results. For the CDs domain, the performance of the CDR models greatly improves with less NT in Figure 4 .From the SNT perspective, information-poor domains have a lower probability of negative transfer, as they are inherently less well-trained. From the HNT perspective, on the Amazon-8 dataset, the performance of all models declines, which we attribute to the strong negative knowledge introduced by the four additional domains compared to the Amazon-4 dataset.On the Amazon-16 dataset, all methods achieve best performance which indicates more knowledge from the source domains can improve the model performance in the CDs domain. Overall, the capability of EMCDR, PriCDR and FedCDR to alleviate negative transfer is much higher than that Table 2 : The recommendation performance on Amazon@Books. Single Domain represents FedGNN and its performance is exactly the same on three sub-datasets. FedGCDR-DP is a complete implementation of our method while FedGCDR does not incorporate Gaussian noise. (The best result for the same setting is marked in bold and the second best is underlined. These notes are the same to others.) Model Amazon-4@Books Amazon-8@Books Amazon-16@Books HR@5 NDCG@5 HR@10 NDCG@10 HR@5 NDCG@5 HR@10 NDCG@10 HR@5 NDCG@5 HR@10 NDCG@10 Table 3 : The recommendation performance on Amazon@CDs.Model Amazon-4@CDs Amazon-8@CDs Amazon-16@CDs HR@5 NDCG@5 HR@10 NDCG@10 HR@5 NDCG@5 HR@10 NDCG@10 HR@5 NDCG@5 HR@10 NDCG@10 Figure 5 : Ablation study on Amazon-16@CDs and Amazon-16@Books.of FedCT. This is because the proportion of target domain features in the final feature is guaranteed by tuning hyper-parameters to control the transfer ratio of the source domain. Meanwhile, FedGCDR avoids this kind of human involvement and maintains performance optimality on three sub-datasets.In conclusion, our experiments show the superiority of FedCDR in recommendation performance and the effectiveness of alleviating NT.To study the contribution of each module of FedGCDR, we implement two model variants, FedGCDR-M and FedGCDR-T. FedGCDR-T transfers the source domain embeddings without mapping. FedGCDR-M replaces the attention graph expansion with the average sum of source domain embeddings and omits the fine-tuning stage. We experiment with Books and CDs as target domains on the Amazon-16 dataset. The experimental results are shown in Figure 5 . We make the following observations: (1) The two variants perform differently on different target domains. On the Books domain, FedGCDR-T performs better than FedGCDR-M, which indicates that for domains with higher data quality, preventing the transfer of negative knowledge from other domains is more important than mapping this knowledge better (in other words, the quality of external information holds greater significance than its quantity), and the Positive Knowledge Activation module meets the requirements of such domains. On the CDs domain, FedGCDR-M performs better than FedGCDR-T, which indicates that for domains that are deficient in information, mapping knowledge correctly is more important than preventing inter-domain negative knowledge (in other words, the quantity of external information holds greater significance than its quality), and the Positive Knowledge Transfer module meets these requirements. (2) Compared to FedGCDR, the absence of either module can cause a significant drop in performance. This indicates that in cross-domain recommendation, we should not only focus on transferring positive knowledge, but also control the spread of negative knowledge to the target domain, especially when a large number of domains.According to the experimental results shown in Table 5 , our FedGCDR achieved the best experimental metrics in both knowledge transfer directions. This shows that our approach is also suitable for dual-domain scenarios where users full-overlap and have only a single source domain and a single target domain.We provide experimental results on privacy budget in Appendix D.2. 
limitations: 
 Our experiments were conducted on 16 domains of the Amazon dataset. While this extensive dataset covers broader source domains, relying on a single dataset may limit the generalizability of our model to data from other sources. Our approach uses overlapping users as a cross-domain bridge. Indeed, there are no widely-recognized cross-domain recommendation datasets with more than three domains, aside from the Amazon dataset. Despite this limitation, we firmly believe that the significant improvements in privacy preservation and model performance demonstrated by FedGCDR underscore its superiority. 
conclusion: 
 We proposed FedGCDR, a federated graph learning framework designed for BS-CDR. FedGCDR addresses the critical challenge of privacy preservation and negative transfer by employing a positive knowledge transfer module and a positive knowledge activation module. Our privacy-preserving method achieves best recommendation quality results on 16 domains of the Amazon dataset. In the future, we aim to extend FedGCDR to improve the recommendation performance of both the target and the source domains. 
acknowledgment: 
 The research was supported by Natural Science Foundation of China (62272403). 
broader impacts: 
 Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?Answer: [Yes]Justification: We have discussed social impacts in Appendix E.Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.• The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?Answer: [NA] Justification: Our method well-address the privacy issure and poses no such risks.Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. • For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. 
 
 Appendix: Due to the algorithmic nature of GNN, the source domain embeddings we pass are a function result on the user embeddings and item embeddings. This means that in the event of a successful inference attack, our user item interaction matrix is exposed to the threat of privacy disclosure. We apply differential privacy (DP) to further safeguard embeddings, following the approach of Cai et al. [53] .Threat model In this paper, we assume the threat model to be semi-honest (honest-but-curious). Under this threat model, the participants adhere strictly to the FL protocol for collaborative model training. However, they are interested in the sensitive rating data and may attempt to extract as much information as possible from the transferred embeddings. Specifically, these semi-honest parties, i.e. the target domain, may employ inference attacks [54] on the embeddings to reconstruct or infer sensitive user-item interaction matrix of other domains.DEFINITION 1 (THE GAUSSIAN MECHANISM). Given a function f : D → R d over a dataset D, the Gaussian mechanism is defined as:EQUATIONwhere r i is the random noise drawn from N ∼ (0, σ 2 ∆ 2 f 2 ) and σ = √ 2ln(1.25/δ) ϵ. In FedGCDR, the intra-domain GAT-based federated model is considered as the function f (•).THEOREM 2. The Gaussian mechanism defined in Definition 1 preserves (ϵ, δ)-DP for each publication step [22] .First, we give the definition of the inverse function: DEFINITION 2 (INVERSE FUNCTION). Given a function f : D → R d over a dataset D, the inverse function f -1 is defined as:EQUATIONwhere e u , e v = Embedding(x), x ∈ D.For the target domain, the embeddings received form source domains can be regarded as the functional result of their models. Let the function be f (•, •) and the input e u , e v is the user embedding and item embedding respectively. The embeddings is the output f (e u , e v ). The target domain attempts to find a inference attack function I(•) which is as close to the inverse function as possible.DEFINITION 3 (PRIVACY LEAKAGE). Given a function f : E → R d over a Embedding set E and an inference function I, the privacy leakage Λ is defined as:EQUATION)whereEQUATION)EQUATION)∥ e -I(f (e u , e v )) ∥ 2 reflects the closeness of the reconstructed input to the true input. Therefore, privacy leakage (PLeak) Λ is able to reflect privacy leakage of FedGCDR with the inference function I(•). PLeak equal to 1 means a perfect reconstruction, and being close to zero means a bad reconstruction. DP on the embeddings further ensures that attackers cannot perfectly reconstruct the raw data.  and I(f (e i u , e i v )) and I(f (e j u , e j v )). This is a contradiction as the perfect reconstruction requires both e i u , e i v = I(f (e i u , e i v )) and e j u , e j v = I(f (e j u , e j v )) to achieve Λ = 1. Therefore, the function f (•, •) must be is bijection. Therefore, by perturbing the source domain embedding with Gaussian mechanism, the reconstructed data of the ideal attack deviates from the real data and prevents a perfect reconstruction (i.e., Λ = 1).Due to the complexity of the FedGCDR pipeline, we perform a theoretical analysis of the computational and communication cost of FedGCDR accordance with the HVH pipeline including horizontal source domain training, vertical positive knowledge transfer module and horizontal positive knowledge activation module.Given a GAT model, let V be the number of nodes, E be the number of edges, and F the embedding size. The computational cost of one propagation layer of classic GAT framework is O(V F F ′ + EF ′ ) [38] . In the horizontal source domain training, our model is a simplified GAT variants which discard feature transformation and non-linear activation. For a N K layer model, the simplified computational cost is O(N K EF ). In the vertical positive knowledge transfer module, space mapping is carried by a N m layers' MLP with computational cost O(N m F 2 ). In the horizontal positive knowledge activation module, the first part is the simplified GAT model and the second part is the fine-tuning model with computational cost O(F 2 ). In conclusion, for the FedCDR framework with N D domains, T G GAT-based federated model training epochs, and T F fine-tuning epochs, the total computational cost isO(T G (N D N K EF + N m F 2 ) + T F F 2 )). Cause N m F 2 ≪ N D N K EF , we get the final computational cost O(T G N D N K EF + T F F 2 )).In FedGCDR, the global model and item embeddings are held by the domain server. Let I be the number of items and F be the embedding size. The space complexity of global model and item embeddings are O(F ) and O(IF ) respectively. In the horizontal source domain training, the domain server distributes the global model and item embeddings and get the gradient with the same size. The communication cost is O(F + IF ). In the vertical positive knowledge transfer module, the N m layers' MLP and its gradients are transmitted with the communication cost O(N m F 2 ). In the horizontal positive knowledge activation module, the target domain additionally perform a fine-tuning stage with communication cost O(IF ). In conclusion, for the FedCDR framework with N u users, T G federated model training epochs, and T F fine-tuning epochs, the total communication cost isO(T G N u (N m F 2 +F +IF )+T F N u IF ). Cause N m F 2 +F ≪ IF , we get O(N u IF (T G +T F )).According to the expression, the communication cost of our FedGCDR is basically equivalent to the cost of two HFL progress. The cost is reduced because knowledge transfer totally takes place in user space, thus avoiding large-scale information exchange.The Amazon dataset we used is the 2018 version and can be easily accessed in https://cseweb. ucsd.edu/~jmcauley/datasets/amazon_v2/. The basis of our domain selection strategy is the amount of data before performing data filtering. Thus, we sorted the domains contained in the Amazon dataset based on the amount of data in descending order and selected the top 16 domains. Similarly, the Amazon-4 and Amazon-8 datasets were selected accordingly. The only exception is that we prioritized the Movie domain, which has a relatively small amount of source data, based on popularity. In addition to multi-domain experiments, we randomly selected 2500 overlapping users in the Books domain and CDs domain to construct the dataset Amazon-Dual, so as to validate the performance of our FedGCDR in the conventional dual-domain scenarios where users full-overlap. The processing details are shown in Table 5 . The bottleneck time of FedGCDR is the federated-GAT training time in each domain, and we also show it in Table 5 . We provide the implemented details of our proposed model and baselines. We set batch size = 256 and latent dim = 8 for all domains. The number of propagation layer of GAT-base federated model is set to 2. The MLP has two hidden layers with size={16, 4}.Considering the trade-off between recommendation performance and privacy preservation, we set ϵ to 8 and σ to 10 -5 . We set α=0.01 and β=0.01 which are the two hyper-parameters of the objective function L GAT (•). When training our models, we choose Adam as the optimizer, and set the learning rate to 0.01 both in GAT-based federated model training and the fine-tuning stage. To evaluate the recommendation performance, we use the leave-one-out method which is widely used in recommender systems [51] . Specifically, we held out the latest interaction as the test set and utilized the remaining data for training. Then, we follow the common strategy which randomly samples 99 negative items that are not interacted with by the user for the rank list generation of the test set. We consider the top-k recommendation task as the main experiment so we choose metrics including Hit Ratio (HR)@K score and the Normalized Discounted Cumulative Gain (NDCG)@K [55] of the top-K ranked items with K=5, 10. We conduct the experiments on three groups of random seeds and report the average results. We conduct all the experiments on NVIDIA 3090 GPUs.D.1 Neagtive transfer on HR@10 and NDCG@10. For HR@10 and NDCG@10 in Figure 6 , our method and baselines show similar trends to the previous HR@5 and NDCG@5. Compared to Figure 6 , the slight difference is that FedCT's HR@10 performance is better on Amazon-8@CDs than on Amazon-4@CDs. We believe that the reason is the poor performance of FedCT on Amzon-4@CDs lowers the threshold for negative transfer of the newly added source domain. To study the effects of privacy budget ϵ on the model performance, we vary the privacy budget ϵ = {4, 8, 16, 32, 64} to affect the σ. We experimented on the Amazon-16 with CDs as the target domain and fix δ = 10-5. We report the results Figure 7 . From that we can observe that the model's performance decreases as ϵ decreases.The degradation in model performance suggests that our approach struggles to counteract the effects of high-intensity noise in a large number of domains, but the model performance is not completely destroyed by Gaussian noise. Thus, there is a trade-off between accuracy and privacy, where a smaller ϵ value adds more noise to embeddings for stronger privacy preservation but leads to more prediction error. Therefore, to balance the data privacy preservation capacity and the model performance, we set it as ϵ = 8.Our proposed FedGCDR is tailored for BS-CDR, focusing on the privacy and negative transfer problems. CDR is widely uesd, while BS-CDR is generic and close to the reality. Our approach can better mine user preferences and effectively protect privacy. On the one hand, users will benefit from more accurate recommendations and thus have a better experience in shopping, watching movies, etc. On the other hand, various economic entities can gain more profits.NeurIPS Paper Checklist 


Decision: This paper proposes FedGCDR, a novel federated graph learning framework for cross-domain recommendation that effectively addresses privacy concerns and the risk of negative transfer. The framework features a positive knowledge transfer module that leverages differential privacy to securely extract reliable domain knowledge, and a knowledge activation module that filters out harmful knowledge to enhance target domain training. Extensive experiments on the Amazon dataset demonstrate that FedGCDR significantly outperforms state-of-the-art methods. Reviewers agree that the problem studied is meaningful and the proposed method has clear merits. We appreciate the authors' efforts in the rebuttal to address reviewers' comments and provide additional experimental results. I recommend acceptance, with the suggestion that the authors carefully address the additional feedback and suggestions raised by reviewers if the paper is accepted at NeurIPS.

Reviews: review: paper_summary: paper_summary: To solve the privacy issue and negative transfer phenomenon in the cross-domain recommendation, the authors propose a novel framework named FedGCDR. Following the HVH pipeline, two key modules collaboratively transfer positive knowledge and filter the negative interference from source domains. In the experimental part, real world data in sixteen domains validated the model's performance.
strength_weakness: strength_weakness: 1.	The introduction and related works sections of the paper introduce two pivotal concepts: intra-domain privacy and inter-domain privacy. However, the definitions provided for these concepts appear to be inconsistent, which may obscure the paper's motivation regarding privacy concerns. For the paper to effectively convey its contributions and significance, it is crucial that these two concepts are clearly and uniformly defined. 
2.	The authors have chosen to emphasize the "Broader-Domain CDR" scenario, which involves more than three domains when defining the problem.  While this distinction is noted, it is not immediately clear why the term "multi-domain CDR" is not used to introduce the framework, as it seems to be a more commonly accepted term in the literature.
questions: questions: In practical application, heterogeneity exists between different domains, and it may not be appropriate to use the same embedding dimension. Is there a possible solution?
limitations: limitations: Please refer to the weak points.
paper_summary: paper_summary: The paper proposes a novel federated graph learning framework, FedGCDR, aimed at addressing the challenges of privacy and negative transfer (NT) in Broader-Source Cross-Domain Recommendation (BS-CDR) scenarios. The framework includes two key modules: the positive knowledge transfer module and the positive knowledge activation module. These modules ensure privacy preservation and mitigate NT by employing differential privacy and feature mapping techniques, followed by graph expansion and fine-tuning in the target domain. The framework is validated through extensive experiments on the Amazon dataset, demonstrating its superior performance over existing methods.
strength_weakness: strength_weakness: 1. Despite the overall clarity of the writing, there are several clerical mistakes that could easily mislead the reader.
2. While the results on the Amazon dataset are promising, it is unclear how well the model generalizes to other datasets or real-world scenarios with different characteristics.
3. The authors emphasize that the contribution lies in provides high-quality BS-CDR recommendations while safeguarding both user privacy and domain confidentiality. However, the experimental section is completely devoid of any discussion on privacy preservation.
questions: questions: 1. About the clerical error in writing, such as in line 105, should "privacy of individual users (inter-domain privacy)" be "intra-domain privacy"? The line 166 “we learn learning…”, and the line 584 “FedCT’s HR@10 performance is better on Amazon-4@CDs than on Amazon-8@CDs”. But according to Figure 6, it should be the Amazon-8@CDs that outperform the Amazon-4@CDs for FedCT's HR@10.
2. In Section 3.1, the authors assumed that users are partially overlapping between domains, so how do non-overlapping users perform graph expansion in the target domain?
3. The improvement in recommendation performance of the proposed framework is demonstrated in the experimental section, but why another important contribution on privacy preservation is not discussed?
4. Does FedGCDR-DP in Tables 1 and 2 refer to another variant method? What is the difference with the proposed FedGCDR? This is not explained relevantly in the paper.
5. Are the Amazon-4, Amazon-8 shown in Table 4 randomly divided? Or is there another domain selection strategy?
limitations: limitations: Yes. The authors have addressed the limitations.
paper_summary: paper_summary: This paper presents a novel federated framework of CDR, FedGCDR, for privacy preserving and negative transfer between domains. Its key strengths include a solid theoretical foundation analyzing the DP-based privacy preserving and a novel and dynamic attention generation method to mitigate negative transfer. By tackling both privacy concern and potential negative transfer problem, this work makes a valuable contribution to the field.
strength_weakness: strength_weakness: 1. The paper addresses the broader-domain CDR involving more than three domains. While multi-domain CDR is undoubtedly significant and has been the subject of extensive research, the emphasis on the number "three" is noted. Could the authors clarify the significance of this number in their work? Does this particular emphasis distinguish their research from existing studies? If not central to the research, it is suggested that the authors reconsider the emphasis on this number to avoid potential misunderstandings or unnecessary focus.

2. By introducing multiple social regulation terms, yet the authors have chosen one specific term (as shown in Equation 9). It would be beneficial for the authors to provide an explanation for this selection. Is this choice based on theoretical analysis, empirical results, or comparative study with existing literature? Clearly articulating the basis for this choice will help readers understand its importance and applicability.
questions: questions: 1. This article is mainly from the user's perspective to solve the privacy problem and whether the proposed framework can be extended to more realistic company-level applications.

2. Negative transfer is an important problem in CDR as well as transfer learning. However, facing the ‘when to transfer’ problem [1] in CDR, it seems to be a potential method to stop the negative transfer when it occurs. Why cannot we adopt this simple method to avoid such complex attention generation progress? 

[1] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on knowledge and data engineering, 2009, 22(10): 1345-1359.
limitations: limitations: Please refer to the weak points.
paper_summary: paper_summary: This paper introduces an innovative federated CDR framework with two key modules tailored for privacy preserving and negative transfer. For privacy, it presents a solid theoretical guarantee. For negative transfer, it generates domain attentions by virtual social links and conduct a fine-tuning stage to filter source domain knowledge. With the comprehensive empirical evaluation, it shows the superiority of the proposed framework.
strength_weakness: strength_weakness: 1. In Figure 2, the use of the terms "extended" and "expansion" appears to be inconsistent.  Consistent use of terminology throughout the paper will aid in the reader's comprehension and the overall clarity of the presentation
2. The authors have conducted extensive experiments on the Amazon dataset as detailed in the experimental section. However, as the authors themselves acknowledge in the limitation section, the generalization performance of their framework has not been sufficiently validated. Given this, and despite the fact that the experiments may not align with the authors' definition of BD-CDR, it is recommended that the authors consider conducting experiments on other datasets (e.g., Douban) to further verify the generalizability of their framework. This additional testing would strengthen the paper's claims regarding the robustness of the proposed approach.
questions: questions: 1. From the perspective of privacy, DP is widely used in previous method. What are the privacy innovations in this paper? What is the unique technical contribution between this work and existing works on privacy-preserving CDR?
limitations: limitations: Please refer to weaknesses.

Comments: comment: Thank you. We appreciate your response, and we are glad that our rebuttal is reassuring.
Thank you for your thorough and detailed responses to the reviews. Your explanations have effectively addressed my concerns, particularly regarding the generalization of the model, the domain selection strategy, and the distinctions between the variant methods. I appreciate the additional experiments and clarifications you've included in the updated version of the paper, which have significantly strengthened the overall quality of this work. In light of these improvements, I will be revising my score accordingly.
Dear Reviewers,

As the deadline for the discussion approaches, we are happy to address any further questions or provide additional clarificaion if needed. Thank you.
Thanks for the detailed explanation. I will keep my score.