Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs
Authors: Qi Hu
Weifeng Jiang
Haoran Li
Zihao Wang
Jiaxin Bai
Qianren Mao
Yangqiu Song
Lixin Fan
Jianxin Li
Venue: Accepted by TMLR ()

Abstract: The increasing demand for deep learning-based foundation models has highlighted the importance of efficient data retrieval mechanisms. Neural graph databases (NGDBs) offer a compelling solution, leveraging neural spaces to store and query graph-structured data, thereby enabling LLMs to access precise and contextually relevant information.  However, current NGDBs are constrained to single-graph operation, limiting their capacity to reason across multiple, distributed graphs. Furthermore, the lack of support for multi-source graph data in existing NGDBs hinders their ability to capture the complexity and diversity of real-world data. In many applications, data is distributed across multiple sources, and the ability to reason across these sources is crucial for making informed decisions. This limitation is particularly problematic when dealing with sensitive graph data, as directly sharing and aggregating such data poses significant privacy risks. As a result, many applications that rely on NGDBs are forced to choose between compromising data privacy or sacrificing the ability to reason across multiple graphs. To address these limitations, we propose to learn Federated Neural Graph DataBase (FedNGDB), a pioneering systematic framework that empowers privacy-preserving reasoning over multi-source graph data. FedNGDB leverages federated learning to collaboratively learn graph representations across multiple sources, enriching relationships between entities, and improving the overall quality of graph data. Unlike existing methods, FedNGDB can handle complex graph structures and relationships, making it suitable for various downstream tasks. We evaluate FedNGDBs on three real-world datasets, demonstrating its effectiveness in retrieving relevant information from multi-source graph data while keeping sensitive information secure on local devices. Our results show that FedNGDBs can efficiently retrieve answers to cross-graph queries, making it a promising approach for LLMs and other applications that rely on efficient data retrieval mechanisms.

Published in Transactions on Machine Learning Research (07/2025)
Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs
Qi Hu The Hong Kong University of Science and Technology
ghuaf@coonect..usttkk
Weifeng Jiang Nanyang Technological Universiby
weifeng001
Haoran Li
hlibt@@commect.uus...
The Hong Kong University of Science and Technology
Zihao Wang
mwanggo@connect.us..kk
The Hong Kong University of Science and Technology
Jiaxin Bai
jbai@ccomect..ust. ust.hk
The Hong Kong University of Science and Technology
Qianren Mao
@aggrag edu.cn
Zhonggancuu Laboratory
Yangqiu Song
yqsonggese.ust
The Hong Kong University of Science and Technology
Lixin Fan WeBank
fanlisin wwwbbnw.eeeeoccoooommm com
Jianxin Li Beihang University
lija buaa. edu.cn
Reviewed on OpenReview: https: // openreview. net/ forum? id- RKR
Abstract
The increasing demand for deep learning- based loundation models has highlighted the im- portance efficient. data retrieval mechanisms. Neural graph databases (NGDBs) offer compelling solution, leveraging neural spaces to store and query graph-structured data, thereby enabling LLMs to access precise and contextually relevant information. However, current NGDBs are constrained to single-graph operation, limiting their capacity to reason across multiple, distributed graphs. Furthermore, the lack of support for multi-source graph data in existing NGDBs hinders their ability to capture the complexity and diversity of real-world data. In many applications, data distributed across multiple sources, and the ability to reason across these sources crucial for making informed decisions. This limita- tion is particularly problematic when dealing with sensitive graph data, as directly sharing and aggregating such data poses significant privacy risks. As result, many applications that rely NGDBs are forced to choose between compromising data privacy sacrific- ing the ability to reason across multiple graphs. To address these limitations, we propose to learn Federated Neural Graph DataBase (FedNGDB), a pioneering systematic frame- work that empowers privacy-preserving reasoning over multi-source graph data. FedNGDB leverages federated learning collaboratively learn graph representations across multiple sources, enriching relationships between entities, and improving the overall quality of graph data. Unlike existing methods, FedNGDB handle complex graph structures and

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lationships, making it suitable for various downstream tasks. We evaluate FedNGDBs on three real-world datasets, demonstrating its effectiveness in retrieving relevant information from multi-source graph data while keeping sensitive information secure on local devices. Our results show that FedNGDBs can efficiently retrieve answers to cross-graph querics, making it a promising approach for LLMs and other applications that rely on efficient. data retrieval mechanisms.
1 Introduction
Graph Databases (GDBs) excel at efficiently storing and managing highly interconnected data, leveraging their graph structure to efficiently manage complex relationships. This capability makes them indispens- able for applications such as recommendation systems (Wang et al. 2019 Cao et al. 2019) and fraud detection (Prusti et al.) 2021 Sadowski & Rathle 2014), where the graph structure is critical. GDBs of- for dynamic data models that adapt to evolving structures and deliver scalable performance for intricate queries. In the era of deep learning-based foundation models, such as large language models (LLMs), their importance has surged with the advent of Retrieval Augmented Generation (RAG), where agents utilize external GDBs, such as knowledge graphs (KGs) to enhance their retrieval capabilities (Gao et al. 2023 Lewis et al. 2020). This integration facilitates the crcation of interactive natural language interfaces tailored to domain-specific applications, enabling more intuitive and accessible interaction with structured dala and
unlocking new possibilities for intelligent data-driven solutions [Matsumoto et al. 2024 Hussien et al. 2025
Jin et al. 2024).
However, traditional graph databases often suffer from two limitations: the ineffectiveness of free-text seman- tic search and graph incompleteness, which are prevalent issues in real-world knowledge graphs and other graph-structured data. Incompleteness leads to the exclusion of relevant results, as the graph database may not capture all the necessary relationships and connections between entities by traversing (Bordes et al. 2013 Galárraga et al. 2013). These limitations hinder their ability to fully support advanced retrieval tasks. To address this, neural graph databases (NGDBs) have recently been proposed by Besta et al. 2022 Ren
ct al. (0023). They integrate the adaptable structure of graph data models with the powerful processing capabilities of neural networks, allowing efficient storage, effective graph-structured data analysis, and flex- ible attribute representation (Zhang al., 2024). NGDBs provide unified storage for diverse entries in embedding space and a neural query engine searching for answers to complex queries from the unified stor- age (Ren et al. 2023). These databases unlock stronger capabilities for intelligent data exploration, allowing users to craft complex queries and make informed inferences with the help of advanced neural network tech- niques [Bai et al. 2025 Among these applications, complex query answering (CQA) is an important yet challenging lask in graph reasoning and can be used to support various downstt ream tasks (Bai el al.; Ren 2023 CQA aims to retrieve answers that satisfy given logical expressions (Hamilton et al.] 2018 Ren ct al. 2020 which are often defined in prodicate logic forms with relation projection operations, existential quantifiers 3, logical conjunctions A, disjunctions V, etc. As shown in Figure I given a logical query 9, our aim is to find all the research topic entities V.2 for which there exist Nobel Prize winners V who were born in Germany and conducted studies in that specific field.
et al.
Although neural graph databases have achieved remarkable success in addressing complex query answering tasks, they are limited to utilizing a single central graph database and cannot be extended to multiple databases. As data plays an increasingly vital role, NGDBs have experienced rapid growth in scale and scope, aggregating knowledge from diverse domains. Consequently, constructing graph database that includes all related entities and relations has become difficult and it is impractical to access central database with all the data needed (Peng et al.) 2021 Chen et al. 2021 Zhang et al. 2022). , Collaborations between various NDGB holders are essential for answering more complicated queries. For example, as shown in Figure I. there are multiple NGDBs with different domain knowledge. A complex query may consist of entities and relations from multiple NGDBs, preventing a local query answering model on a single database from answering those cross-gres gros- queries. However, there are various reasons hindering the data sharing between NGDB holders. For example, the growing attention on privacy, regulations such as the General Data. Protection Regulation
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NGDB-Nobel Prize
NGDB-Academic
NGDB-Social
Plank
Einstein
Schrödinger
Nobel Prize
Physics
BomIn
Embedding
Storage
Win
Collaboration
Germany
Bohr
Study
Nomination
Einstein
Einstein
Query Engine
Query
q V, 3V: Win(V, Prize) Born/n(V, Germany) Study(V, Vn)
Interpretation
Find what research topics which Nobel Prize winner who was born in Germany studied.
Figure An example of cross graph queries on distributed neural graph databases. The relations and entities in KG complex query can be from NGDBs which cannot be solved in single local database.
(GDPR), and commercial interest between data holders, etc. Distributed database approaches exist NNdda et al. 2021). but they fail leverage NGDBs' neural capabilities fully.
Federated lcarning offers potential solution, enabling collaborative model training across distributed par- ticipants without sharing raw data. (McMahan et al. 2017 Yang al. 2019 In federated knowledge graph embeddings, raw graph triplets are retained on local devices, participants train local models, and only gradients are transferred to learn a global graph embedding model (Chen et al. 2021 Zhang et al.) 2022: Peng et al. 2021) under the protection of privacy preservation methods, such as homomorphic encryption (HE) (Paillier 1999), secure multi-party computation (SMPC) (Mohassel Zhang 2017), and differential
privacy [work 2008 Geyer al. 2017). While federated learning has been widely applied in learning graph embeddings, recent studies have indicated that learned representations can still potentially leak privacy (Hu) infer sensitive information from the learned em-
& Song 2023 Duddu et al. 2020), where attacker
beddings, besides, existing works only focus on learning high-quality representations for simple downstream
tasks, such as knowledge graph completion (Chen et al. 2021 Zhang et al. 2022 Huang et al.) 2022 Tang
2023 and lacks the ability to reason over graphs and retrieving answers to complex queries. ct al.
To overcome these gaps, we propose to learn Federated Neural Graph DataBase (FedNGDB), novel system that can reason over multi-source graphs avoiding sensitive raw data transmission and embedding exposure to safeguarding privacy. FedNGDB
be applied to different, central complex query answering models. It leverages federated learning to train local query answering models in local NGDBs and align graph embeddings for global queries. Different from other federated graph embedding models, the FedNGDB server not only takes the responsibility of aggregating global models but also decomposing given complex queries to sub-queries to compute the query encoding and retrieve answers from distributed NGDBs under the protection of multi-party computation to avoid global model storage. Meanwhile, to better evaluate distributed NGDB systems' performance, we create a benchmark on three widely used datasets. We evaluate our proposed FedNGDBs the benchmark, the experiment results show the effectiveness of retrieving answers to complex queries from multi-source graphs. We summarize our major contributions follows:
To the best of our knowledge, we are the first extend federated graph embedding systems complex query answering tasks, which is critical for graph holders' collaboration.
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Based on three public datasets, propose benchmark for evaluating the retrieval performance of distributed NGDB systems. The benchmark systematically evaluates the retrieval performance facing cross-graph queries.
We propose FedNGDB, a federated neural graph database system that can retrieve answers of com- plex queries from distributed NGDBs with privacy preserved, solving the drawbacks of existing federated knowledge graph embeddings methods. Extensive experiments on three datasets demon- strate its high performance when facing cross-graph queries.
The rest of the paper is organized as follows. We review the related works in Section Section introduces the preliminary and definition of the distributed complex query answering systems. Section introduces the framework of Fe in detail. Section evaluates the performance FedNGDB on the benchmark based on three real-world datasets. Finally, we conclude our work in Section
2
Related Work
2.1 Neural Graph Database
Neural Graph Databases (NGDBs) neutralize traditional GDBs' storage and query planning modules, aiming for stronger intelligent data exploration capabilities. Complex query answering (CQA) crucial task in NGDBs, which involves training model to process and answer complex logical queries based on graph reasoning, process known as query encoding. These methods represent complex queries into various structures and effectively search for answers among candidate knowledge graph entities. GQB (Hamilton et al., 2018), Q2B (Ren et al. 2020), and HypeE (Liu et al. 2021 encode queries to vectors, hyper-rectangle and hyperbolic embeddings, respectively. To support negation operators, various encoding methods are proposed. Q2P (Bai et al., 2022) and ConE (Zhang al. 2021) use multiple vectors to represent complex queries. BetaE (Ren Leskovec 2020), Gammaα (Yang al., 2022), , PERM (Choudhary et al., 2021) propose to various probabilistic distributions to encode complex logic graph queries. Some methods, like LMPNN (Wang et al. 2022) CQD (Arakelyan et al. 2020), Var2Vec (Wang et al. 2023a take pre-train enbeddings on simple link prediction tasks and apply logic operators to answer complex queries. Mcanwhile, neural structures are utilized to encode complex queries: BiQE [Kolnis el al. 2021 and Kg KgTrrnsformer (Liu
et al.
2022 are proposed to use transformers, SQE [Bai et al.) applies sequential encoders, GNN-QE ZZhu
ct al.
graph neural networks to encode queries, respectively. There are also encoding methods proposed to encode various types of knowledge graphs: NRN (Bai et al.) 2023b) is proposed to encode numerical values, MEQE (Bai et al. 2023a) extends logical
2022) and StarQE (Alivanistos
al.! 2021 use message-passinng
queries over events, states, and activities. P-NGDB (Hu et al. 2024 first extends the privacy preservation to neural graph databases.
While there are numerous existing complex query answering methods, these methods mainly focus on a single large graph. There are some methods proposed to reason over multi-view and temporal and vary- ing graphs: MORA (Xi et al., (2022) ensembles multi-view knowledge graphs to scale up complex query answering, TTransE (Leblay & Chekol 2018) and TRESCAL (Wang et al., 20155) can be applied on tem- poral knowledge graphs. However, these methods need raw data transmission and privacy protection is not considered. Conducting privacy-preserving complex query answering on multi-source knowledge graphs is still unexplored. With the growing attention on privacy and data protection, sensitive data cannot circulate freely among data holders, and complex query answering is forced to be conducted collaboratively on mul- tiple knowledge graphs. Our research introduces federated learning to existing complex query answering so that we can apply reasoning over distributed NGDBs without raw data sharing.
2.2 Federated Knowledge Graph Embedding
In recent years, federated learning has emerged as a promising approach to address privacy and scalability concerns in machine learning. It allows data owners to participate in model co-construction without raw data transmission to reduce privacy leakage risks (McMahan al. 2017 Yang al. 2019) under the protection of privacy protection techniques such differential privacy (DP) YYang et al. 2019), homomorphic encryption
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(HE) Zhang et al. 2020), secure multi-party computation (SMPC) Byrd & Polychroniadon 2020 Various studies have been conducted to explore the potential of federated learning in different domains, such as recommender system YYang et al. 2020), finance (Long et al. 2020) etc. Federated databases are proposed to manage distributed data allowing storing and querying databases with privacy preservod [Bater et al.,
2017 Berger & Schrefl, 2008 2013). Although some federated graph databases are proposed to manage graph-based data (Nadal et al., 2021 ; the study for the latest neural graph databases are still be ignored.
Federated knowledge graph embedding is another related topic. It tries to represent entities and their seman- tic relations into embedding spaces. FedE (Chen et al. 2021) learns knowledge graph embeddings locally and aggregates all local models in a global server for higher representation quality. FedR (Zhang et al. 2022 proposes to learn representation with privacy-preserving relation aggregation to avoid privacy leakage risks in entity embedding and reduce communication costs. FedCKE (Huang et al., 2022), FedMKGC (Tang ct al. 2023) extend federated learning to learn global representations from different domains and multilingual knowledge graphs. FedEC (Chen et al., 2022b applics contrastive learning to tackle data heterogeneity in
knowledge graphs. MaKEr (Chen et. al. 2022a) and MorseE (Chen et al. 2022c) utilize mela-learning to
transfer knowledge among knowledge graphs to train graph neural networks for unseen knowledge extrapo- lation and inductive learning. DP-FLames (Hu et al.
2023) quantifics the privacy threats and incorporates
Waan et al. 2023b) decomposes the embed- ding matrix and enables the sharing of latent representations to reduce the risks of privacy leakage and communication costs, FedM (Hu et al. 2022a) splits the duty of aggregating entities and relations to reduce the risks of graph reconstruction attacks. FKGE (Peng et al.) 2021) applies differential privacy and avoids the need for a central server. While existing federated knowledge graph embedding methods are proposed to distributedly learn high-quality representations with privacy preservation, there some works indicate that the learned embeddings are informative and vulnerable to various privacy attacks (Hu et al.] 2022b), even in federated scenarios (Hu & Song 2024 Hu et al., (023).. Besides, these methods all lack the ability to answer complex queries on multi-source knowledge graphs which is critical for more complicated downstream tasks. Our research expands the simple federated knowledge graph embedding to answer complex queries on distributed knowledge graphs.
private selection in federated knowledge embeddings.
FLEST
3 Preliminary and Problem Formulation
3.1 Preliminary
Following the general setting of federated knowledge graph embeddings, we denote a set of graph-structured data from various data owners G {g1 .2....,99................ 9N3, J. where N is the total number of graphs. Data owners have their own graph data and cannot access to other's databases. Let 9r (Vs, Rg, Tx) denotes the k-th graph in G, where Va denotes the set of vertices representing entities in the graph Jk. R2 denotes the set of relations, Tk denotes the set of triplets. Specifically, To = { r. vt)} C V x Rr x V4 denotes there is a relation between Up and vt, where Uh, Ut E Vs, r E Rx. We denote V = U Vs. R = U / Rr: T U To as the set of vertices, relations, and triplets of all graph data, respectively.
3.2 Complex Logical Query
The complex logical query is defined in existential positive first-order logic form, consisting of various types of logic expressions like existential quantifiers 3, logic conjunctions A, and disjunctions V. In the logical expression, there is set of anchor entities V, E V denotes given context, ixistential quantified variables Vi, V2.,... 1/2 E V, and a unique variable Vn denotes our query target. The complex query intends to find the target answers Vi2 E V, such that there are Vi, Vi E V in the graph-structured data that can satisfy the given logical expressions simultaneously. Following the definition in Ren et al. (2020), the complex query expression can be converted to the disjunctive normal form (DNF) in the following:
<[V]] = Vi,,, V : Vo V. = Gi, A 622 A... A Cim,
(1)
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where ei is the atomic logic expression, which can be the triplet rV V") denotes relation r between entities V and Vi, oj is the conjunction ion of several atomic logic expressions Cig- V, V are either anchor entities existentially quantified variables.
Decryption
Train
Embeedinggs
Eneryption
Perturbed Embeddings
Séaret
Answer
Aggregation
Operator Network
Graph Data
Local NGDB
Operator Netwerk
?
Client
Epplad/Distribuur
Query Encoding
Complex Quary
Decryption
000
Embedelnggs
Eneryption
Answer Scores
SSarre
Aggregation
Apgregation
Operator Network
Quety
Graph Data
Local NGDB
Query Decomposition
Client
Server
Figure 2: The training and retrieval process of FedNGDB. The blue line denotes the training process, and the green line denotes the retrieval process. In the training, clients' NGDB models are trained on respective graph--ttructure data. At each round, local NGDBs are aggregated at the server and updated using the global parameters. Among them, the embeddings are protected by secret aggregation so that the server can not access them. In the retrieval, each query is decomposed into sub-queries. Clients compute sub-query embeddings which the server is used to aggregate query embeddings. Answer scores computed at clients and are aggregated at server to retrieve answers.
3.3 Distributed Graph Set Query
Graph databases are owned by different data holders and cannot be shared directly with each other, therefore, complex query q can involve entities and relations from different graphs. We define those queries as follows:
Definition 3.1 (Cross-graph Query) complex query is a cross cross-graph query if there exists query an- swers V E V, such that there are Vi. V E in the graph that can satisfy the given logical expressions and the alomic expressions in the query can not be found in single graph.
For example in figure 1 the entity 'Physics" is the answer to the query because there exists existentially quantified variable "Einstein' that can satisfy the logical expression. The query is a cross-graph query as the atomic expressions in the query are from different graph databases and can not be found in a single graph. For example, Win(Finstein, Nobel Prize) and BornIn(Einstein, Germany) are two atomic expressions from different graph databases. When the distributed graph databascs face cross- pooss-graph queries, the answer can not be inferred from single graph. Besides, We have the definition of in-graph query correspondingly:
Definition 3.2 (In-graph Query) A complex query q is an in-gruph query if for all unswers V V to the query, such that there are Vi.-.. ,V, e V in the graph that can satisfy the given logical expressions and the atomic expressions in the query are from single graph database.
These in-graph queries can retrieve answers according to one of the graphs in the graph set and can be solved with existing complex query answering models. However, a distributed graph neural graph database system, the in-graph queries may retrieve more answers more knowledge is provided.

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3.4 Problem Formulation
Given a graph-structured data set 9 = {9...... IN with N graphs. Every graph is owned by independent data holders and can not be shared to construct a unified graph database. We assunee that the triples are sensitive as they describe the informative relations between entities while the index of entities and relations can be shared, which means that the triples in each graph database will stay private local devices. The graphs in the G are related and have part of entities and relation overlapped. There are complex queries involving elements from the graph set 9 and can be classified as cross-graph queries and in-graph queries. We aim to construct a distributed neural graph database system to reason over multi-source graphs and retrieve answers to complex logical queries while koeping privacy preserved, especially the triple information indicating the relations between entities. To achieve this, we assume that there is an honest but curious server managing the federated neural graph database system. Because the learned embeddings are vulnerable to various privacy attacks, the embeddings cannot be exposed to the server and should be further protected before being transferred to the server.
4
Federated Neural Graph Databases
In this section, we introduce the learning and query retrieval process of our proposed FedNGDB.
4.1 Model Learning
We first introduce the training of FedNGDB. As shown in Figure 2 FedNGDB has a central server and set of clients. Each client has graph with overlapping entities of others. The server takes the responsibility of aggregating parameters and organizing the training and retrieval process. The clients train a local NGDB model based on their graph-structured data. According to the sensitivity of the parameters, we divide the query encoding methods into two parts: operator function with parameter θ and entity embeddings E. For the operator function, the client directly sends the parameter to the server for aggregation and receives the global function to update the local operator function, which is similar to FedAvg (MeMahan al.] 2017). Therefore, in the following parts, we only introduce the entity embeddings aggregation in FedNGDB.
4.1.1 Secret Aggregation
There are various techniques, like homomorphic encryption (HE) Paillier 1999 secure multi-party com-
putation (MPC) (Mohassel & Zhangg 2017), and differential privacy (Dwork 2008 Geyer et al. 2017) to protect the uploaded paramcters, however the protection of aggregated global model are often ignored. Un- fortunately, the global model is informative and vulnerable to privacy allacks (Zhang el al., 2022 Hu & Song 2024). Hence we propose a secret aggregation applied parameter aggregation which can prevent the global server from knowing the aggregated paramcters using homomorphic encryption. Assume that at cach client parameter denoted as 0, is uploaded to server for aggregating global parameter O. The proce- dure of secret aggregation is described in Algorithm 1 In the beginning, each client C₁ randomly generates perturbed parameters Or and shares them with other clients with encryption (Shown in Appendix (A). After the sharing, each client has a set of parameters (0 ...,.................. ,,,)). In the training process, for each client, C, uploads perturbed parameter (0, + O.)) to the server under the protection of homomorphic encryption. The server collects all perturbed parameters from clients, computes aggregated perturbed paramcter 8", and sends it back to all clients. Finally, after receiving the perturbed parameters, the clients first decrypt the parameters and can compute aggregated parameter 0 by removing the perturbed parameters and preventing exposing it to the server.
Besides, the paramelers are also protected by the differential privacy to prevent the potential privacy leak- age (Zhu et al.) 2019), we use local differential privacy (LDP) to the local gradients to further improve the difficulty to recover sensitive information from transmission. As proposed in YY et al. (202T), we clip the local gradients based on the Loo norm with the threshold C and apply zero-mean Laplacian noise with strength X, the upper bound of the privacy budget is 2C
X. The detailed discussion can be found in Appendix B.3
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4.1.2 Model Training
Similar to Chen et al. (2021), the server constructs set of mapping matrices {M' E {0, 10 J=n and existence vectors (v' E {0, [ to denote the entities in each client, where is the number of all unique entities in KG set and ny is the number of entities from client C. M Mm, = 1 if the m-th entity in entity table corresponds to the n-th entity from client C.. v = indicates that the m-th entity in entity table exists in client C-.
FedNGDB performs secret aggregation for entity embeddings. First, the client C2 will randomly initialize the local entity embeddings Eg E Ra and perturbation embeddings E, E R Every client will share the permutation embeddings with all clients. At round t, the server will sclect part of the clients C participating in the training. After local training of CQA on respective local graphs, client C₁ sends perturbed local entily embeddings E; + E, to the server. The server will aggregate the entity embeddings using perturbed local enbeddings:
E
10
v' 2 EM'(E iEC
+ +,,,
(2)
where denotes all-one vector, @ denotes element-wise division for vectors and & denotes element-wise multiply with broadcasting. After aggregation, the server sends the aggregated entity embeddings back to all clients, and the client C2 receives:
E M E
(3)
and the client C, can compute and update the local entity embeddings as:
E < E T
10 10 1 JEC
wl
& ΣME
(4)
JEC
After secret aggregation, the entity embeddings of all clients shared without exposing the sensitive information to the server. Besides the training of entity embeddings, the operator networks are trained and aggregated using FedAvg MMcMahan et al. 2017), and the detailed descriptions of the FedNGDB are shown
in Algorithm 2
4.2 Query Retrieval
After training, the server in FedNGDB manages the process retrieving answers to complex queries. The server first tries arrange related clients to encode the coming queries and retrieves answers from all local graph databases based on the encoding.
4.2.1 Query Encoding
Query encoding methods commonly represent queries to embeddings and retrieve answers according scoring functions where similarity functions are widely used. FedNGDB encodes queries and treats two types of queries differently. For in-graph queries, these queries only involve single graph data, FedNGDB directly encode queries using corresponding local complex query answering models. For pusss-graph queries, as the query involves entities from multiple graphs, the server will take the responsibility to plan the entire encoding process: First, the server will decompose the query to alomic expressions and send each expression to corresponding local graphs. The clients encode received atomic queries using their own local CQA models and send back the results to the server. The server collects all the encoding results and uses global operator function models compute the representations of the queries. The query is iteratively updated by communicating between the server and clients until the original queries encoded.
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4.2.2 Answer Retrieval
Because the entity embeddings are not stored in the central server, can only retrieve answers from all distributed local graphs after encoding the queries. Given a query encoding we score all the candidate entities at each local graph database, at client C,:
S c (50. ERaX
(5)
where f score function in the client C.. Then the score will be uploaded to the server to aggregate a score table for all unique entities in the graph sets:
N
N M'S.
-
10
Σ
(6)
The final answers to the queries are retrieved globally from the graph database set according to the score table.
5
Experiments
In this section, create benchmark of distributed graph complex logical query answering problems for distributed neural graph databases and evaluate our proposed FedNGDB's performance on the benchmark.
5.1
Datasets and Experiment Setting
We introduce the detailed information of our used datasets and the setting of our experiments.
1p
2p
2i
3i
pi
2u
up
Figure 3: The query structures used for evaluation in the experiments. Naming for each query structure is provided under each subfigure, for brevity, the p, and u represent the projection, interscction, and union operations respectively.
5.1.1 Datasets
In experiment, following previous work, the three commonly used knowledge graphs in central graph reasoning lasks graph-structured data: FB15k (Bollacker et. al. 2008 Bordes et al. 2013) FB15k- 237 (Toutanova & Chen 2015). and NELL995 (Carlson et al. (010)) to construct the distributed query answering benchmark, which can make better comparison to existing works. In each dataset, there are vertices describing entities and edges describing relations. To evaluate the distributed complex query, we follow the common settings of federated knowledge graph emebddings (Chen et. al., 2021: Zhang et al. 2022), conducting experiments assuming having 3 and clients in each lederated neural graph database system, respectively. For more clients, also conduct simple exeperiments shown in Appendix C.1 We randomly select relations into clients and split triples into clients according to selected relations as local graph databasc. The triplets in each local graph are separated into training, validation, and testing with a ratio of 8:1:1 respectively. Following previous works (Ren et. al. (220),, we construct training graph Gtrain. validation graph Gvel, and test graph Grest in each client by training edges, w validation
edges, and edges, respectively. The detailed statistics are listed the table [ #Clients denotes the number of clients, #Nodes, #Relations, #Edges denote the average number of nodes, relations, and edges in each client respectively.

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Table 1: The statistics of three datasets used for experiments.
Graphs
# #Clients
#Nodes
#Relations
#Edges
13,651
79
103,359
FB13k-237
12,639
47.4
62,015
3
14,690
448.3
197,404
FB15k
5
14,279
269
118,442
40,204
66.7
47,601
NELL995
28,879
40
28,560
Table 2: The statistics of queries sampled from three datasets used for experiments.
In-graph Valid.
Cross-graph
Graphs
#C
Train.
Test.
Test.
3
317,220
11,528
11,539
32,573
FB15k-237
5
180,552
6,619
6,673
31,469
3
592,573
19,206
19,267
53,660
FB15k
344,418 5
11,409
11,437
53,154
3
208,070
8,810
8,750
24,954
NELL995
5
117,231
5,177
5,118
24,237
5.1.2 Query Sampling
Following previous work (Hamilton al.) 2018 Bai al., 2023b we evaluate the complex logical query answering performance the following eight general query types with abbreviations of 1p, 2p, 2i, ip, 3i, pi, 2u, and up. As shown in figure 3 each subgraph denotes query type, where each edge represents either a projection or a logical operator, and each node represents either a set of entities, the anchor entities and relations are to be specified to instantiate logical queries. We use the sampling method commonly used in previous works Bai et al. Ren et al. (2020) to randomly sample complex qucries from graphs. We randomly sample two sets of queries from the graph sets: in-graph queries and cross-graph queries to evaluate local and global answer retrieval performance. For local model evaluation, first obtain training, validation, and testing queries from the formerly constructed local graph databases respectively. Then for the training queries, conduct graph search to find corresponding training answers the local training graph. For the validation queries, we search for the answers both the training graph and the validation graph and only use those queries that have different numbers of answers on the two graphs. For the testing queries, we use those querics that have different answers on the testing graph from answers on the training graph and validation graph. For global model evaluation, we construct global training, validation, and testing graphs using all local graphs, and sample testing queries with atomic expressions from different local graphs, finally we search for answers on three global graphs and only use those queries that have different answers on the testing graph from other two graphs. We collect statistics of complex queries in three datasets and the statistics are shown in Table 2 The number of in-graph queries is the average number of the client's 's local queries.
5.1.3 Basclines
We can use various existing query encoding methods our local base model, to evaluate the effectiveness and generalization ability of our proposed FedNGDB, we select three commonly used complex encoding methods GQE (Hamilton et al. 2018), Q2P (Bai et al.) 2022), Tree-LSTM (Bai et al.) as our base model. GQE is a graph query encoding model that encodes a complex query into a vector in embedding space; Q2P represents complex queries using multiple vectors; Tree-LSTM recursively represents complex queries and treats all operations, entities, and relations as tokens.
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The retrieval performance of distributed neural graph databases when there are 3 clients. The average results of HR@3 and MRR of all clients reported. The best results are underlined. The bost results of distributed models are in bold.
Table 3:
GQE
Q2P
Tree-LSTH
Craph
Sctting
In-graph
Cros-graph
In-graph
Cross-graph
It-graph
Cross-graph
HR
MRR
HR@3
MRR
HR
MRR
HRØ3
MRR
HR83
MRR
HR83 MRR
Local
12.64
12.03
14.35
13.63
13.32
12.73
Central
13.13
12.39
13.03
12.28
14.93
14.66
15.02
14.81
13.28
12.61
13.36
FB15k-237
FedE
13.72
13.23
12.74
11.63
14.82
14.27
14.79
13.93
13.12
12:23
12.62
12.08
FodR
12.89
11.98
14.32
14.23
13.92
12.92
FedNGDB
13.54
12.43
12.63
11.32
15.32
14.32
14.83
14.11
12.93
12:11
12.55
11.96
Local
22.05
18.21
24.32
22.64
22.87
20.51
Central
29.53
25.65
30.21
25.33
38.62
34.14
38.03
34.36
38.87
36.86
37.97
36.13
FB16k
FedE
24.31
26.74
27.95
25.21
43.68
39.62
39.72
35.95
34.27
30.18
31.19
26.03
FolR
20.29
18.61
25.32
22.71
23.64
20.97
FedNCDB
25.63
26.87
24.77
25.17
44.02
39.27
40.27
36.31
34.85
33.83
31.80 80
28.99
Local
11.85
11.03
13.86
13.02
13.85
13.85
12.94
Central
12.87
11.95
13.06
12.46
16.74
14.82
16.42
15.63
15.41
14.23
16.27
15.83
NELL995
FedE
13.29
12.72
12.46
11.82
17.23
14.12
16.28
14.01
14.27
13.81
14.18
13.71
FodR
12.01
11.23
16.04
13.26
12.48
11.67
FedNCDB
14.21
13.27
13.76
12.67
16.62
15.28
16.27
16.23
16.28
15.38
16.09
15.27
Table 4: The retrieval performance of distributed neural graph databases when there are 5 clients. The average results of HR@3 and MRR of all clients are reported. The best results are underlined. The best results of distributed models are bold.
GQE
Q2P
Tree-LSTM
Graph
Setting
In-graph
Cross-graph
In-graph
Cross-graph
In-graph
Cross-graph
HR@3
MRR
HR@3
MRR
HR@3
MRR
HR83
MRR
HRØ3
MRR
HRØ3 MRR
Local
11.44
10.65
14.65
11.23
FB15k-237
Central
13.72
12.87
12.99
12.74
15.93
14.62
15.74
15.23
13.62
12.54
11.86
11.53
FedNGDB
12.42
11.60
11.20
10.79
16.13
15.78
15.28
14.91
12.48
11.91
11.49
11.02
Local
19.83
17.51
36.10
35.04
20.03
18.62
FB15k
Central
20.58
20.14
21.21
21.02
42.56
41.87
39.57
39.46
24.88
24.76
24.93
24.72
FedNCDB
21.40
20.83
20.71
19.94
40.81
37.96
38.56
35.73
24.59
23.75
23.85
22.90
Local
10.48
10.09
15.26
14.37
14.52
13.89
NELL995
Central
14.56
14.28
13.75
13.42
15.63
15.14
15.27
14.75
16.47
15.92
15.74
14.47
PodNGDB
13.79
13.27
12.74
12.18
15.44
15.28
14.24
15.68
14.28
14.57
12.89
To the best of our knowledge, there are no existing lederated complex query answering methods but sev- eral federated knowledge graph embedding methods, therefore, we choose to compare our methods with FedE (Chen al. 2021) and FedR (Zhang et al. 2022), two commonly used federated knowledge graph embedding methods as baselines. FedE aggregates both entity embeddings and relation embeddings in a server, while FedR only aggregates relation embeddings for privacy concerns and communication efficiency. We utilize these two methods with slight modifications to train a global complex query answering model: FedE aggregates all query encoding parameters and FedR aggregates relation embeddings and query encod- ing networks. Besides, we also compare our FedNGDB with local and central settings. These two baselines represent the lower bound and upper bound, respectively, terms of the amount of information available to the training system. In the local setting, there is no collaboration between clients, while in the central setting, all distributed graphs in the graph set aggregated for global graph for training, we sample complex queries from global training, and validation graphs for training and validation.
If there no further stalement, we use the following implementation sellings in the experiments. We tune hyper-parametee parameters on the validation local queries for the base query encoding methods and set the dimension of entities and relations as 400 for all models for fair comparison and use AdamW (Loshchilov & Hutter 2018) as optimizer. We the gradient clip threshold C' 0.1 and Laplacian noise scale 0.2 achieve 1-DP.
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5.1.4 Evaluation Metrics
Following the previous work Bai et al. 2023b), we evaluate the generalization capability of models by caleulating the rankings of answers that cannot be directly retrieved from an observed graph. Given a lesting query q, the training, validation, and public testing answers are denoted as Merain, Meat, and Miett respectively. We evaluate the quality of retrieved answers using Hit ratio (HR) and Mean reciprocal rank (MRR). HR@K metric evaluates the accuracy of retrieval by measuring the percentage of correct hits among the top K retrieved itemss The MRR metric evaluates the performance of a ranking model by computing the average reciprocal rank of the first relevant item in a ranked list of results. The metric can be defined as:
Metric(q)= |Messt / Maal
E
m(rank(v)).
ve /
- S K] if the metric is HR@K and m(r) 1 if the metric is MRR. Higher values denote better reasoning performance. We train local models at each client by using the in-graph training queries and tune syper-parameters using the validation queries. The evaluation is then finally conducted on the testing queries, including the evaluation of in-graph queries on local query encoding models and cross-graph queries on the global federated neural graph database system, respectively.
5.2 Performance Evaluation
We evaluate FedNGDB's complex query answering performance on three datasets and compare to other basclines. We apply FedNGDB three base query encoding models and evaluate the average retrieval performance on various queries. The results are summarized in Table 3 and Table A Table 3 reports the retrieval performance of various distributed graph complex query answering models when there are 3 clients. For each model, we evaluate performance facing in-graph queries and cross-graph queries, respectively. For in-graph queries, the average scores of all clients are reported. We report results in HR@3 and MRR which higher scores indicate better performance. The best results are underlined. The best results of distributed models are in bold. As shown in Table B our proposed methods can effectively retrieve complex query answers from distributed graph databases. In comparison to local settings, we can see that. FedNGDB can utilize all participated local graph databases and performs better facing in-graph queries. For example, GQE model with FedNGDB can achieve 14.21 HR@3 on average while can only reach 11.85 without collaboration. Besides, in comparison to other federated knowledge graph embedding methods, our proposed FedNGDB can reach comparable performance in both in-graph queries and cross-graph queries without exposing sensitive entity embeddings to the server. For example, FedNGDB achieves the best performance in cross-graple queries in more than half of datasets and base query encoding models.
In Table 4 we present the performance of FedNGDB when there are 5 clients. We compare the performance with local training without collaboration to demonstrate the influence of client nunbers. As shown in the table, FedNGDB performs well compared to complex query answering models without collaboration, there are performance improvements in all datasets after applying FedNGDB to various base query encoding models, demonstrating that FedNGDB can utilize the intrinsic information in the distributed knowledge sets. The collaboration allows FedNGDB to reason over various logical paths to improve performance.
5.3 Query Types
FedNGDB globally reason distributed graphs and retrieve answers to cross-graph queries. To evaluate FedNGDB's performance on various types of complex queries, we conduct experiments to evaluate the retrieval performance of FedNGDB and compare it to central learning on FB15k-237 when there are 3 clients. Figure {(a) shows the FedNGDB-GQE's performance facing cross-graph queries. As we can see from the figure, FodNGDB performs well on most various types of queries compared to the central model. For example, on query types '2i' and 'pi', FedNGDB can reach more than 90% MRR compared to the central model.
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35
Central
18
Local
30
28.23
27,53
27.53
FedNGDB
FedNGDB
14
12.94
[3:41
13.25
21.88
12
11.36
12.18
11.25
12.11
19.31
MRR
20
MRR
9.81
15
13,91
8
7.01
7.89
10
4.31
5.73
5.65
1818
6.24
4
2
2p
21
31
2u
Query Type
Clients
(a) Cross-graph Queries
(b) In-graph Queries
Figure 4: The evaluation results of FedNGDB-GQE facing cross-graph queries on FB15k-237 (subligures (a)).
The evaluation results of FedNGDB-GQU facing in-graph queries on FB15k-231 (subfigures (c)).
5.4 Local Influence
Because the in-graph queries can be processed by single local neural graph database, in this part, we evaluate the performance of FedNGDB on these queries to assess the influence of FedNGDB on local queries. We conduct experiments on FB15k-237 and the number of clients 5. We evaluate the performance of FedNGDB based on GQE and compare the model with no collaboration. The results are summarizod the Figure [bb] As shown in the figure, although each client has different performance due to the triplets correlation being different in each sub-dataset, FedNGDB can improve all clients' performance compared to the model without collaboration.
6 Conclusion
In this work, present federated neural graph database, FedNGDB to reason over distributed knowledge sets with privacy preservation, allowing graph database holders to collaboratively build distributed graph reasoning system without sharing raw data. We define the distributed graph complex logical query answering problem. To solve the problem, we propose secret aggregation for federated learning where the aggregated parameters can be kept secret to the server. Besides, we design a distributed query retricval process for answering queries from distributed graph database sets to protect clients' privacy. To evaluate FedNGDB model performance, we construct benchmark based three commonly used knowledge graph complex query answering datasets: FB11k-237, FB15k, and NELL995. Extensive experiments demonstrate the effectiveness of our proposed FedNGDB. FedNGDB can retrieve answers given a query while keeping the sensitive information secret at local graph databases. In the future, we aim to propose new mothods for better answering complex queries by exploiting intrinsic information in the distributed neural graph databases.
the benchmark
Acknowledgement
The authors of this paper were supported by the ITSP Platform Research Project (TTS/180/23FD) from ITC of Hong Kong, SAR. China, and the AoE (AoE/E-601/24-N)) the RIF (R6021-20) and the GRF (16205322) from RGC of Hong SAR, China.
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Published in Transactions on Machine Learning Research (07 (07))
A Parameter Sharing
In this section, we provide an example of sharing secrets between clients. The parameters can be shared under the protection using various encryption methods, for example, the commonly used is Diffe-Hellman key exchange Diffiie & Hellman 2022 shown as follows, we consider the sharing process between two clients:
Client A and Client B publicly agree to modulus p and base 9, p is prime.
Client A chooses a secret integer a, then sends Client B mA = g" mod p.
Client B chooses secret integer b, then sends Client A mg go mod p.
Client A computes m mod p.
Client B computes m mod p.
After D-H key exchange, Client A and B share secret s 9oh mod p. The secret s can be used encryption to share sensitive information between clients.
B Alogrithm
B.1 Secret Aggregation
We present the pseudo-code of secret aggregation in Algorithm
Algorithm 1: Secret Aggregation
Require: n clients C1 C,,..., Cn. client C; has parameter Each client Cs generates random parameter 05 Transmit O to all other clients with encryption, each client has set of parameters (0,,0%. 09.,
Client C₁:
Upload perturbed parameter (0, + 8.) to server with HE Encryption.
Receive encrypted parameters ()=1(0
(0, + 05) from server and HE decryption.
Compute enerypled parameters = 10 + +07) Σ, 0r)/n
Server:
i= n do
Receive encrypted parameters (0, 07) from client Ca
end for
Compute encrypted parameters 6" = Em= + 87)
Send encrypted parameters 0" to all clients
B.2 FedNGDB Framework
We present the pseudo-code of FedNGDB in Algorithm
B.3 Differential Privacy Setting
Differential privacy represents a mathematical framework for quantifying and limiting the disclosure of pri- vate information in statistical databases (Dwork 2008). In federated learning, differential privacy enables collaborative model training across decentralized data sources while providing formal privacy guarantees. By injecting calibrated noise into the training process, these techniques ensure that individual contributions
19

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Published in Transactions on Machine Learning Research (07 (025)
Algorithm 2: FedNGDB Framework
Require: The number of clients N; The faction of clients selected in each round F;
Client C₁:
Client C initialize entity embeddings E' and perturbation embeddings E.
Share E, with other clients with encryption
Receive and decryption to get _E..... ,E,}
Upload E' to server for secret aggregation, receive Ej
Server:
Server constructs permutation matrices (M,) and existence vectors {1))a= and initialize operator networks O, distribute to all clients.
for t= 0, do
Server distributes operator networks to each client.
< Randomly select client set with N F clients.
for C. E C. in parallel do
(E; + E, + ClientUpdate(C;; M "M, E,, θ,)
end for 1 Eiec, & ( E,)
0++1 + 1//C// Ciec, 0
end for
UlientU C, E", O);
E E M
1 Ø E Ejec & Mie
for e = 1, ... E do
E. e, + LocalUpdate(E,,0 9) end for return E+ E
cannot be reliably identified or extracted from the global model (Geyer et al. 2017 The privacy-utility tradeoff can be tuned via the privacy budget parameter e, allowing practitioners to balance learning per- formance against disclosure risk. The formal definition of e-differential privacy (c-DP) is follows (Dwork) 2006): A randomized algorithm M satisfies e-differential privacy if for all neighboring datasets Dj and D2 that differ in at most one record, and for all possible outputs S S Range(M):
Pr(M(D)) E S S e Pr(M(D2) S
Where the privacy parameter that quantifies the privacy loss.
In the former part, we introduce Laplacian noise to the local gradients to meet the guarantee of e-DP for in-graph query in local neural graph databases. However, for cross-graph query, compute the score on each graph database for each query. The Post-Processing Theorem (Dwork et al. 2014) which states the composition a data-independent mapping with an cDDP algorithm M is also cDP. Since the ranking operation of FedNGDB a post-processing step applied to the outputs (D), ... MM,(D) from each local databases and the overlapped candidate answers share same scores, by the post-processing theorem of differential privacy, the final ranked list maintains the same privacy guarantee of e-D..
C
Auxiliary Experiments
Here present some auxiliary experiments to further evaluate the performance of FedNGDB.
20

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Published in Transactions on Machine Learning Research (07 (07/2025)
C.1 More Clients
In the former experiments, evaluate FedNGDB's performance when there are 3 or 5 clients in the federated system. To further evaluate models' performance when there are more clients. We split the graph-structured data into 10 subgraphs and evaluate the retrieval performance of FedNGDB using GQE as the base model. As shown in Table FedNGDB can still improve the retrieval performance compared to local training when there are more clients participating the distributed system, demonstrating the effectiveness of FedNGDB.
Table The performance of GQE when #C=10.
FB15k-237
FB15k
NELL995
Graph
Setting
In-graph
Cross-graph
In In-graph
Cross graph
In- In-graph
geassgraah
HR:33
MRR
HR83 MRR
HR83
MRR
HRØ3 MRR
HR@3
MRR
HRØ3 MRR
Local
8.96
8.47
13.46
13.04
8.36
7.83
CQE
Central
13.15
12.76
13.24
12.89
19.76
19.43
20.27
19.87
13.76
13.28
13.53
13.27
FedNGDB
10.67
10.17
10.23
10.04
15.39
14.98
14.86
14.45
12.93
12.11
12.55
11.96
C.2
Relation Overlap
In the experiments, we evaluate the retrieval performance when there are overlap relations between local graph databases, however, various graph databases can have shared relations in real life, to evaluate the performance in such a scenario, we evaluate the FodNGDB with GQE's retrieval performance. The graph- structured data randomly split into subgraphs. The results are shown Table showing that FedNGDB can successfully retrieve answers from distributed graph databases.
Table 6: The MRR of GQE when relation overlapped.
FB15k-237
FB15k
NELL995
Graph
Setting
In-graph
Cross-graph HR83 MRR
In-graph
Cross-graph HR83 MRR
In-graph
Cross-graph
HRØ3
MRR
HR83
MRR
HR@3
MRR
HRØ3 MRR
Local
10.48
10.22
20.76
20.21
10.03
9.64
GQE
Central
13.47
13.27
13.86
13.54
30.74
30.46
30.27
30.16
13.82
13.68
13.56
13.12
FedNGDB
11.96
11.42
11.67
11.43
23.89
22.47
23.22
22.89
11.68
11.36
12.03
11.89
C.3
Convergence Rate
We evaluate the convergence speed of three federated frameworks. The results are presented by the average number of communication round ratios relative to FedE. As shown in Table 7 FedNGDB's convergence speed is faster than FedR while slightly slower than FedE, demonstrating that our FedNGDB can protect stronger protection while remaining competitive efficiency.
Table The statistics of communication rounds
Setting
FedE
FedR
FedNGDB
Relative Rounds to FedE
1.00
1.32
1.09
21


Reviews:
[{"review_id": "bOopsiPOf9", "reviewer": "Reviewer_kdka", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "## **Strengths**\n\n* The proposed task is significant and requires further research attention.\n* The overall framework is intuitive and achieves promising results.\n\n---\n\n## **Weaknesses**\n\n* **Methodology**\n   \n  The methodology section is difficult to follow and lacks essential details. My concerns are as follows:\n   * What distinguishes the proposed FedNGDB from existing federated learning methods applied to graph learning or graph database systems? Section 4.1.2 appears to be a simple extension.\n   * How are cross-graph and in-graph queries classified? It seems labels are assigned during the query process, but an automatic discrimination between the two types of queries is crucial.\n  * The process by which the server decomposes queries into atomic expressions is not adequately discussed. If expressed in natural language, this process resembles task planning in LLM-based agents, yet its concrete execution remains unclear.\n   * (Minor point) Figure 2 could be improved; for different clients, the graph data should vary.\n\n* **Experiments**\n   * **Benchmark Creation**: Creating a benchmark for cross-graph queries is a significant contribution. However, technical details need justification, such as the choice of 3 and 5 clients, whether the query structures represent all real-world scenarios, and how data quality is controlled or assessed.\n   * **Performance**: According to Table 3, FedNGDB does not consistently achieve satisfactory performance and often underperforms compared to baselines like FedE and FedR. Additionally, the omission of these baselines in Table 4 raises questions.\n   * For Figure 4, since its purpose is comparison, it would be beneficial to combine (a) and (b) with (c) and (d) into two separate figures. The current separate presentation only allows observation of individual methods' trends, hindering direct comparison and analysis between the two methods.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces **a novel task** setting focused on answering queries from distributed knowledge graphs. This task is particularly relevant given the rapid growth of real-world graph systems that involve multi-source data and the necessity for cross-graph queries while ensuring privacy through federated learning.\n\nTo address this task, the authors extend federated learning techniques, enabling privacy-preserving reasoning across multi-source graph data. Additionally, they propose **a new benchmark** that includes both in-graph and cross-graph queries, effectively addressing the current evaluation gap for such tasks.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please refer to weaknesses", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "WD0aiwg8Un", "reviewer": "Reviewer_x3N4", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths:**\n- The paper is well-written, and the figures are clear and enhance understanding.\n- The experimental setup and results are described with clarity and sufficient detail.\n- A diverse set of experiments is conducted, enabling meaningful comparisons with several existing methods.\n\n**Weaknesses:**\n- Some claims in the manuscript appear overly strong. I would recommend moderating the language used to describe the results. For example, the manuscript states that FedNGDB can effectively retrieve solutions for all benchmarks in Table 3. However, it is not evident from Table 3 that FedNGDB consistently outperforms the other methods. Similarly, the comparisons shown in Figures 4(a) and 4(b) do not clearly demonstrate superior performance of FedNGDB.\n- The only difference between Table 3 and Table 4 is the number of clients, so it should be possible to evaluate FedE and FedR in Table 4 as well. However, the results for these methods are missing. Is there a reason why they were not included? It would be important to assess whether the performance of FedNGDB improves with an increasing number of clients, especially in comparison to existing methods.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes FedNGDB for answering complex queries across distributed graph databases while preserving data privacy. \nFedNGDB enables collaborative reasoning over multi NGDBs using federated learning and secure computation.\nThis paper also introduce a three benchmarks to evaluate the performance of complex queries that require information between multi graph databases, claiming that FedNGDB is effective on these benchmarks.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please see the weakness section.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "ulzY2sHUYb", "reviewer": "Reviewer_RKTW", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Pros\n\n- This manuscript extends federated graph embedding systems to complex query answering tasks, which is critical for graph holders’ collaboration.\n- Based on three public datasets, this manuscript proposes a benchmark for evaluating the retrieval performance of distributed NGDB systems. The benchmark systematically evaluates the retrieval performance facing cross-graph queries.\n- This manuscript proposes FedNGDB, a federated neural graph database system that can retrieve answers of complex queries from distributed NGDBs with privacy preserved.\n\n### Cons\n\n- The experimental results lack comprehensive analysis. For example, Table 3 shows that FedNGDB does not consistently achieve the best performance. Specifically, when GQE is used as the graph query encoding model on the FB15k-237 dataset, FedE outperforms FedNGDB. The authors should provide an explanation for this phenomenon.\n- The evaluation is overly simplistic and lacks persuasiveness. It is limited to the FB15k-237, FB15k, and NELL995 datasets, all of which are similar in scale regarding the number of training, validation, and test triples. As a result, the experiments do not convincingly demonstrate the effectiveness of the proposed method on larger knowledge graphs.\n- The evaluation is limited to scenarios involving only three or five clients in the federated learning system, without any justification for selecting such a small number. The authors should clarify why configurations with more clients, such as ten, were not considered.\n- While it is commendable that the authors acknowledge privacy concerns in federated learning, they omit any discussion on backdoor attacks[1, 2, 3], which represent a significant security threat in such systems.\n- The authors introduce a parameter-sharing mechanism within the proposed federated learning framework; however, they do not present any experimental results to assess the time overhead associated with this process.\n\nThere are some missing references:\n\n[1] Liang et al. A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal. TPAMI 2024.\n\n[2] Yu et al. G${}^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering. arXiv 2023.\n\n[3] Yu et al. GZOO: Black-box Node Injection Attack on Graph Neural Networks via Zeroth-order Optimization. IEEE TKDE 2024.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The manuscript addresses the limitations of current NGDBs, particularly their inability to reason across multiple, distributed graphs and handle multi-source data while preserving privacy. To overcome these challenges, the authors propose FedNGDBs, a novel framework that integrates federated learning to enable privacy-preserving, cross-graph reasoning. FedNGDBs enhance relational understanding across diverse data sources without compromising data security. Empirical evaluations on three real-world datasets demonstrate that FedNGDBs effectively retrieve relevant information for cross-graph queries, offering a robust solution for large language models and other applications requiring secure, efficient data retrieval.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please refer to the cons", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)