Privacy Protected Multi-Domain Collaborative Learning

1 INTRODUCTION . Unsupervised domain adaptation ( UDA ) ( Tang et al. , 2020 ; Jiang et al. , 2020 ; Zhang et al. , 2020 ) attempts to transfer knowledge from well-labeled source domains to annotate unlabeled target samples , which have significant domain discrepancy with source domains due to the various data collection manners and devices . Recent explorations ( Na et al. , 2021 ; Dong et al. , 2020 ) suppose the model to be trained has access to both source and target data during the training stage . With such basic assumption , it becomes possible to measure the domain discrepancy and adopt metric-based solutions ( Kang et al. , 2020 ) or domain confusion ( Cui et al. , 2020 ; Tang & Jia , 2020 ) to generate domain-invariant features . However , the hypothesis violates the concerns of practical application on privacy protection , and fails to be deployed to small devices with limited storage . This requirement motivates source-free domain adaptation ( SFDA ) , where the source-supervised model is available to assist the target domain without any source data ( Liang et al. , 2020 ; Li et al. , 2020 ; Kundu et al. , 2020 ) . Generally , SFDA either adapts target samples to source-like ones ( Liang et al. , 2020 ) or generates fake source samples from source-model by subsequently taking UDA strategies ( Kurmi et al. , 2021 ) . To improve the training efficiency , FADA ( Peng et al. , 2020 ) employs a federated learning paradigm ( Karimireddy et al. , 2020 ; Chen et al. , 2020 ) by allocating the target domain on a centralized server while keeping multiple source ones as clients . However , this approach is vulnerable to attacks as the source features transition to target domain . Further , these domain adaptation works ignore the improvement of model generalization on source domain , which is inconsistent with requirement of reality . For example , the long-standing hospitals already have well-annotated patients ’ data , while other newly-built hospitals just collected data without annotation , which need help from long-standing hospitals with well-annotated data due to the huge labeling cost . Besides , with geographical restriction , different hospitals only record their local patients ’ data resulting in various population statistics , causing model bias for long-standing hospitals . Inspired by the above observation , we introduce a more practical scenario called Privacy Protected Multi-Domain Collaborative Learning ( P2MDCL ) ( shown in Figure 1 ) . Specifically , P2MDCL assumes that the well-annotated source and unlabeled target domains are distributed across different clients and there exists a global server merely communicating with each client and integrating the received model parameters from clients . Finally , the server broadcasts the consensus model to all clients for their use to reach the win-win deal . The key challenge for P2MDCL is to learn a more generic model by solving two core issues : 1 ) how to achieve domain alignment during iterative communication ; and 2 ) how to enhance discriminative feature learning . In this paper , we propose a novel Mask-Driven Federated Network ( MDFNet ) to address P2MDCL . First , our MDFNet introduces two orthogonal masks following high-level features in each client to activate domain-invariant and domain-specific semantics respectively . In practice , we minimize the confusion of these two masks to achieve high-quality feature separation and semantic complementary . Second , the unlabeled target client adopts adaptive self-supervised optimization to learn more discrimina- tive representations via pseudo labels generation . Finally , MDFNet adopts a progressive weighting scheme to balance the effect of each client in model integration on the server , which discoveries more knowledge of the labeled client to adjust the model of unlabeled client during the initial communication rounds , then the mature unlabeled client model also yields positive effect on the feature learning of labeled client . The main contributions of our work are summarized as : • First , we are the pioneers to take into account the “ win-win ” and privacy requirements under unsupervised domain adaptation scenarios by introducing Privacy Protected MultiDomain Collaborative Learning ( P2MDCL ) . • Second , we propose an effective algorithm MDFNet to fight off the domain shift in a federated training mechanism , which reaches the win-win deal for all involved domains . • Finally , we derive the generalized error bound for our method , which theoretically testifies the rationality of MDFNet . Moreover , extensive experimental results and analysis empirically illustrate the effectiveness of our method on solving P2MDCL . 2 RELATED WORK . Domain Adaptation . Unsupervised domain adaptation ( Cui et al. , 2020 ) attempts to build a model with well-labeled source and unlabeled target data at hand , by mitigating the domain mismatch . Along this line , the recent explorations mainly adopt discrepancy metric-based method ( Yan et al. , 2017 ; Tzeng et al. , 2014 ) and adversarial training scheme ( Zhang et al. , 2019 ; Tzeng et al. , 2017 ) to learn domain-invariant features . Although these solutions effectively reduce the influence of domain discrepancy , the practical applications difficultly permit the co-existence of source and target data due to the limited storage of small device and data privacy . The demand stimulates the development of source-free domain adaptation ( Liang et al. , 2020 ; Kurmi et al. , 2021 ) , which merely provides the well-trained source model for knowledge adaption on target domain . In addition , Peng et al . ( 2020 ) respectively considers target domain and multiple source domains as the centralized server and clients and adopts federated learning fashion to achieve domain adaptation with multiple discriminators , which is vulnerable to the attack due to the source and target features transmission into the discriminators in the centralized target domain . Even though these strategies actually achieve the comparable transferring ability with the UDA solutions , empirical studies illustrate the current domain adaptation techniques fail to learn a generalized model for source and target domains . Alternatively , they only focus on the improvement of target performance , yet neglecting any benefit to source domain . To this end , this paper posts a novel and practical scenario privacy protected multidomain collaborative learning ( P2MDCL ) , where source and target domains are both regarded as clients independently communicating with the server which produces and broadcasts the consensus model to clients for their use . Federated Learning ( FL ) . FL allows multi-clients collaboratively to complete the same task without data currency across clients ( Yang et al. , 2019 ) . Along with this concept , recent works mainly focus on semi-supervised scenario ( FSSL ) where FedMatch ( Jeong et al. , 2021 ) allocates unlabeled data on client side and labeled data in the server while FedIRM ( Liu et al. , 2021 ) only deploys them on various clients . But they both assume the instances across all client are sampled from the identical distribution . Moreover , Smith et al . ( 2017 ) ; Liu et al . ( 2020 ) explore FSSL with non-i.i.d by supposing each client contains several well-annotated instances for training . Differently , our considered P2MDCL closely approximates the reality , which involves several clients without any annotations and exists significant domain discrepancy across all clients . 3 THE PROPOSED METHOD . 3.1 PROBLEM DEFINITION AND MOTIVATION . The P2MDCL scenario assumes there are L well-annotated source clients Dli = { ( xl ( i ) j , y l ( i ) j ) } nli j=1 ( i ∈ { 1 , · · · , L } ) and U unlabeled target clients Duk = { xu ( k ) j } nuk j=1 ( k ∈ { L+ 1 , · · · , L+ U } ) , where x and y denote an input sample and its ground-truth label , respectively . The instances of these clients come from different distributions but share the identical category space and clients are not allowed to exchange private data with each other . Akin to federated learning , the additional global server in P2MDCL collects and assembles all clients ’ network parameters to form the consensus model . The main motivation of P2MDCL is addressing the negative effect of insufficient training samples in Dli and label shortage in Duk to reach a “ win-win ” deal across all clients . We face two challenges to solve P2MDCL : 1 ) how to reduce the significant distribution discrepancy while protecting data privacy and 2 ) how to learn more generic and discriminative representations from unlabeled target clients . To this end , this work proposes an effective Mask-Driven Federated Network ( MDFNet ) , which deploys mask-driven disentanglement to locally seek domain-specific/invariant features , and explores the adaptive self-supervised optimization to promote the discriminative ability of unlabeled target clients . 3.2 MASK-DRIVEN DISENTANGLEMENT . Feature separation is a commonly-used strategy in domain adaptation to disentangle latent representation into domain-specific features and domain-invariant ones ( Bousmalis et al. , 2016 ; Peng et al. , 2019 ) . However , they typically develop two separated networks to extract the corresponding features , which increase storage burden for each local device with insufficient computational resources . Peng et al . ( 2019 ) points out the high-level neurons from feature extractor actually involve domain- specific and invariant knowledge . Inspired by ( Chattopadhyay et al. , 2020 ) , we explore the binary mask to achieve feature disentanglement by activating the interested neurons . For the brevity , we omit the symbols l/u and ( k ) in the following illustration . As Figure 2 shows , each client of our MDFNet contains a basic feature encoder parameterized θe mapping the raw input into the hidden space via gi = θe ( xi ) ∈ Rd . Subsequently , two additional parameters m̂s , m̂I ∈ Rd are introduced into the local network and activated to form the mask probabilities by using the sigmoid function σ ( · ) to get ms = σ ( m̂s ) and mI = σ ( m̂I ) . For each feature gi , based on the mask probabilities , we sample the binary domain-specific and invariant masks ( msi , m I i ∈ { 0 , 1 } d ) from the Bernoulli distributions . To this end , we obtain the domain specific and invariant features with the element-wise multiplication⊗ over binary masks and features , i.e. , gsi = msi ⊗gi and gIi = mIi ⊗gi . Moreover , we adopt three strategies to achieve high-quality feature separation . Concretely , each client firstly minimizes the semantic overlap between gIi and g s i to store complementary information in them . Motivated by Rahman & Wang ( 2016 ) , we design the following soft-interactive loss as : Ls = ∑ i 〈gsi , gIi 〉 sum ( gsi + g I i − gsi ⊗ gIi ) , ( 1 ) where 〈 , 〉 means the inner product of two feature vectors , and sum ( · ) represents the sum of all elements for a vector . This approximately reflects the information overlap of two mask distributions . The minimization of soft-interactive loss gradually increases the difference between msi and m I i which activate different neurons . Similar to DSN ( Bousmalis et al . ( 2016 ) ) , each client also develops the individual classifier θc ( · ) taking domain-invariant features as input to the category probability distribution θc ( gIj ) . The cross-entropy loss between ground-truth and prediction intensifies the discriminnative ability of domain-invariant features . On the other hand , we also feed the combination of gsi and g I i into decoder θd ( · ) to reconstruct the original input with Lr = ∑ i ‖θd ( gsi , gIi ) − xi‖22 . Thus , the overall loss function of mask-driven disentanglement for labeled clients is formulated as : min θc , θe , θd , m̂s , m̂I Llo = ∑ i −yi log ( θc ( g I i ) ) + Lr + Ls , ( 2 ) where we actually adopt straight-through estimator ( Bengio et al. , 2013 ) to progressively optimize m̂s and m̂I instead of the discrete binary masks leading to the invalid back-propagation .

Summary: This paper studies the problem of privacy-protected multi-domain collaborative filtering, in which a "win-win" deal for source and target domains can be achieved. The proposed framework, MDFNet, contains multiple local clients and one global server. In each client, the encoder achieves feature separation, and the decoder constructs original data based on separated features. Experiments on benchmark datasets demonstrate the best performance of the proposed MDFNet compared with baselines.