Federated Neural Collaborative Filtering
Collabative Filtering - Matching Consumers with Products and Services with Privacy
In this June 2021 paper, the authors introduce a federated version of the Neural Collaborative Filtering (NCF) model, dubbed FedNCF, for personalised item recommendations in a privacy-preserving manner.
This federated approach allows the learning process to occur without requiring users to share or transmit their raw data, addressing privacy concerns and adhering to regulations like GDPR.
The paper explores how to overcome challenges associated with federated learning, particularly regarding the transmission of client updates, by integrating a secure aggregation method that protects against potential privacy breaches by honest-but-curious entities.
The authors highlight the limitations of existing federated aggregation algorithms, particularly in the context of latent factor model updates used in collaborative filtering (CF).
What is a latent factor?
In the context of collaborative filtering (CF) and matrix factorization (MF), a latent factor refers to an underlying characteristic or feature that influences the interactions or preferences within the data, but is not directly observable in the raw data.
Latent factor models aim to discover and leverage these hidden factors to make predictions or recommendations.
The paper's objective
The authors propose an improved aggregation method that decomposes the aggregation step into matrix factorization and neural network-based averaging, demonstrating faster convergence and maintaining recommendation quality.
The proposed FedNCF system is tested against traditional NCF methods, showing that it can achieve comparable recommendation accuracy while enhancing user privacy and reducing computational costs.
Related Work
This section of the paper provides an in-depth discussion on related work:
Matrix Factorization (MF)
Collaborative Filtering (CF) uses past user behaviours to recommend new items. Users and items are represented in matrices, and MF decomposes the user-item interaction matrix into two lower-dimensional matrices, capturing latent factors of users and items.
The similarity between users or items can be measured through the inner product of these latent factor matrices, enabling the system to predict user preferences and suggest relevant items.
Neural Collaborative Filtering (NCF)
Traditional MF uses inner product to estimate interactions, which may not efficiently capture user similarities.
The NCF framework enhances this by using deep neural networks to learn the interaction function, improving recommendation accuracy and model convergence.
NCF includes models like Generalized Matrix Factorization (GMF), Multi-Layer Perceptron (MLP), and their combination (NeuMF), offering both linear and non-linear predictive capabilities.
Federated Learning (FL)
FL is a distributed machine learning approach allowing multiple entities to collaboratively train a model without sharing their raw data, enhancing user privacy.
This decentralized environment relies on local model training on users' devices, with only model updates (not raw data) being sent to a central server for aggregation.
This approach is particularly useful for preserving privacy and reducing the need for data centralisation and transmission.
In federated learning, the algorithm is sent to the local devices, where individual models are trained on local data. These local models are then aggregated into a global model, usually by averaging the updates, which is then shared across the devices.
This process iteratively improves the global model while keeping the data localised, thus addressing privacy concerns and reducing communication costs.
Federated Averaging (FedAvg)
FedAvg is a popular aggregation technique in FL where client updates are averaged to update the global model.
This method, however, is primarily suited for neural network models and not directly applicable to CF tasks due to inconsistencies it may introduce in updates for latent factor models.
Privacy-Preserving Federated Learning
Despite FL's privacy advantages, transmitting raw updates can still leak information. Techniques like Secure Multi-Party Computation (SMC), homomorphic encryption, and differential privacy are discussed to enhance privacy during aggregation.
The paper focuses on the SecAvg protocol, an SMC approach, which maintains privacy without heavy computational costs or quality degradation.
Privacy-Preserving Recommenders
Various methods are summarised that aim to maintain user privacy in CF settings, either centralixed or federated, including approaches using SMC, homomorphic encryption, and differential privacy.
The paper contrasts these methods with their proposed approach, emphasizing the need for efficient, privacy-preserving aggregation mechanisms in FL environments to avoid issues like staleness and computational burdens.
In conclusion, the section provides a comprehensive review of existing methodologies in CF and FL, setting the stage for their proposed FedNCF system, which aims to combine the strengths of NCF within a federated and privacy-conscious framework.
A federated version of Neural Collaborative Filtering (NCF)
The paper introduces FedNCF - which maintains user privacy while generating item recommendations.
Here's a detailed breakdown of the discussed federated neural collaborative filtering, the problem it addresses, and the FedNCF framework:
Problem Definition
The scenario involves multiple users, each with a private dataset, aiming to collaboratively build a horizontal federated recommender system without revealing their raw data.
Interactions are local, and data distributions across users can vary, posing statistical challenges. Data are considered non-iid (independent and non-identically distributed) across users.
The goal is to generate a top-N recommendation list for each user based on local computations without compromising privacy.
FedNCF Framework
FedNCF consists of three federated algorithms:
FedGMF (Generalized Matrix Factorization)
FedMLP (Multi-Layer Perceptron)
FedNeuMF (a fusion of FedGMF and FedMLP)
Users and items are represented as vectors (embeddings), and the NeuMF model, which FedNeuMF is based on, combines linear and non-linear components from GMF and MLP for better recommendation accuracy.
The framework operates under a central server that coordinates model training, with users (clients) selected randomly to update model weights locally and then send these updates to the server.
Weights Aggregation
The FedAvg algorithm is popular for weight aggregation in FL, but its direct application to CF, particularly with latent factor models like MF, can lead to suboptimal convergence due to the item-specific nature of updates.
The paper proposes MF-SecAvg, an enhancement that adjusts the aggregation process to account for the number of users interacting with specific items, aiming for faster convergence and higher recommendation quality.
To address privacy concerns associated with transmitting raw updates, the SecAvg protocol (a Secure Multi-Party Computation scheme) is integrated, enabling the server to aggregate updates without accessing individual user contributions.
Privacy Considerations
The FedNCF system, particularly with MF-SecAvg, respects user privacy by avoiding raw data transmission and leveraging secure aggregation to prevent potential inference of private interactions by the coordination server.
The system is designed to operate over a secure communication channel and incorporates mechanisms to ensure user privacy while still enabling collaborative model training.
In essence, FedNCF introduces a sophisticated approach to collaborative filtering in a federated learning environment, balancing the need for personalised recommendations with stringent privacy requirements, and enhancing the traditional NCF with a federated learning paradigm for improved, privacy-conscious recommendations.
Here's a distilled explanation of the key processes
Item Profile Update
Each user updates only the item profile components corresponding to their interactions.
Users generate random matrices to mask their updates before sending them to the coordination server, which aggregates these updates.
The aggregation process accounts for the number of users interacting with each item, enhancing accuracy and convergence speed while maintaining user privacy.
Users also mask a vector indicating their item interactions, allowing the server to determine interaction counts without compromising privacy.
Neural Architecture Update
The process for updating neural network weights is simpler: users mask their updates post-local training using similar randomization techniques.
The coordination server aggregates these updates in a manner akin to FedAvg but with added privacy via masking.
Computation and Communication Cost
Generating and processing the random matrices/vectors for masking introduces some computational overhead, but it's manageable.
Communication costs are not significantly higher than in standard federated learning setups, as only the masked updates are transmitted.
Privacy and Security
The MF-SecAvg protocol within FedNCF ensures user updates remain confidential, with only aggregated data exposed to the coordination server.
The framework aims to preserve k-anonymity, ensuring individual updates cannot be traced back to fewer than k users.
While robust against most threats, there's a potential vulnerability wherein a coordination server could attempt to breach k-anonymity through manipulative participant selection across rounds.
In essence, FedNCF introduces a sophisticated method to perform neural collaborative filtering in a federated setting, emphasising user privacy while striving to maintain the integrity and utility of the recommendation system.
The integration of secure aggregation protocols ensures individual user interactions remain confidential, showcasing a forward-thinking solution to the challenges of privacy-preserving collaborative filtering.
Relevant Associated Papers
2021: Yang et al. [5] and Zhang et al. [13] discuss federated learning (FL) concepts and applications. They emphasise the distributed nature of FL, where multiple entities collaboratively train a model without sharing data, thus enhancing privacy. The papers also touch on challenges like data distribution and statistical hurdles in FL environments.
2020: Gao et al. [7] and Chai et al. [15] delve into privacy threats in federated matrix factorization and propose secure federated matrix factorization techniques to mitigate privacy risks. They highlight the importance of protecting user data in the aggregation process and suggest methods like secure aggregation to ensure user privacy.
2019: Melis et al. [8] and Ammad-uddin et al. [11] examine unintended feature leakage in collaborative learning and present a federated collaborative filtering method for privacy-preserving personalised recommendation systems, respectively. They underscore the potential privacy risks in collaborative settings and offer solutions to prevent information leakage.
2018: Phong et al. [14] and Wei et al. [16] explore privacy-preserving deep learning via homomorphic encryption and federated learning with differential privacy. They discuss the trade-offs between privacy preservation and model utility, highlighting the challenges in achieving both in a federated setting.
2017: McMahan et al. [6] and He et al. [9] introduce the concept of communication-efficient learning in decentralised data and neural collaborative filtering. They propose architectures that leverage neural networks to enhance recommendation systems, emphasizing the balance between model complexity and communication efficiency.
2009: Rendle et al. [4] present Bayesian Personalized Ranking from Implicit Feedback, focusing on generating item recommendations based on user interactions. They highlight the effectiveness of Bayesian approaches in handling implicit feedback data.
2008: Mnih and Russ [3] explore probabilistic matrix factorization, emphasizing its utility in capturing user preferences and enhancing recommendation accuracy.
2013: Bobadilla et al. [2] provide a comprehensive survey on recommender systems, detailing various collaborative filtering techniques and their evolution over the years.
1997: Resnick and Varian [1] discuss recommender systems in their early stages, explaining the fundamental concepts and the significance of such systems in filtering and suggesting relevant content to users.
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