Gradient Boosting Decision Tree(GBDT) is a widely used statistic model for classification and regression problems. FATE provides a novel lossless privacy-preserving tree-boosting system known as SecureBoost: A Lossless Federated Learning Framework.
This federated learning system allows a learning process to be jointly conducted over multiple parties with partially common user samples but different feature sets, which corresponds to a vertically partitioned data set. An advantage of SecureBoost is that it provides the same level of accuracy as the non privacy-preserving approach while revealing no information on private data.
The following figure shows the proposed Federated SecureBoost framework.
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Active Party
We define the active party as the data provider who holds both a data matrix and the class label. Since the class label information is indispensable for supervised learning, there must be an active party with access to the label y. The active party naturally takes the responsibility as a dominating server in federated learning.
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Passive Party
We define the data provider which has only a data matrix as a passive party. Passive parties play the role of clients in the federated learning setting. They are also in need of building a model to predict the class label y for their prediction purposes. Thus they must collaborate with the active party to build their model to predict y for their future users using their own features.
We align the data samples under an encryption scheme by using the privacy-preserving protocol for inter-database intersections to find the common shared users or data samples across the parties without compromising the non-shared parts of the user sets.
To ensure security, passive parties cannot get access to gradient and hessian directly. We use a "XGBoost" like tree-learning algorithm. In order to keep gradient and hessian confidential, we require that the active party encrypt gradient and hessian before sending them to passive parties. After encrypted the gradient and hessian, active party will send the encrypted [gradient] and [hessian] to passive party. Each passive party uses [gradient] and [hessian] to calculate the encrypted feature histograms, then encodes the (feature, split_bin_val) and constructs a (feature, split_bin_val) lookup table; it then sends the encoded value of (feature, split_bin_val) with feature histograms to the active party. After receiving the feature histograms from passive parties, the active party decrypts them and finds the best gains. If the best-gain feature belongs to a passive party, the active party sends the encoded (feature, split_bin_val) to back to the owner party. The following figure shows the process of finding split in federated tree building.
The parties continue the split finding process until tree construction finishes. Each party only knows the detailed split information of the tree nodes where the split features are provided by the party. The following figure shows the final structure of a single decision tree.
To use the learned model to classify a new instance, the active party first judges where current tree node belongs to. If the current tree belongs to the active party, then it can use its (feature, split_bin_val) lookup table to decide whether going to left child node or right; otherwise, the active party sends the node id to designated passive party, the passive party checks its lookup table and sends back which branch should the current node goes to. This process stops until the current node is a leave. The following figure shows the federated inference process.
By following the SecureBoost framework, multiple parties can jointly build tree ensemble model without leaking privacy in federated learning. If you want to learn more about the algorithm, you can read the paper attached above.
SecureBoost uses data parallel learning algorithm to build the decision trees in every party. The procedure of the data parallel algorithm in each party is:
- Every party use mapPartitions API interface to generate feature-histograms of each partition of data.
- Use reduce API interface to merge global histograms from all local feature-histograms
- Find the best splits from merged global histograms by federated learning, then perform splits.
Hetero SecureBoost supports the following applications.
- binary classification, the objective function is sigmoid cross-entropy
- multi classification, the objective function is softmax cross-entropy
- regression, objective function includes least-squared-error-loss、least-absolutely-error-loss、huber-loss、 tweedie-loss、fair-loss、 log-cosh-loss
- Column sub-sample
- Controlling the number of nodes to split parallelly at each layer by setting max_split_nodes parameter，in order to avoid memory limit exceeding
- Support feature importance calculation
- Support Multi-host and single guest to build model
- Support different encrypt-mode to balance speed and security
- Support missing value in train and predict process
- Support evaluate training and validate data during training process
- Support another homomorphic encryption method called "Iterative
- Support early stopping in FATE-1.4, to use early stopping, see Boosting Tree Param
- Support sparse data optimization in FATE-1.5. You can activate it by setting "sparse_optimization" as true in conf. Notice that this feature may increase memory consumption. See here.
- Support feature subsample random seed setting in FATE-1.5
- Support feature binning error setting
- Support GOSS sampling in FATE-1.6
- Support cipher compressing and g, h packing in FATE-1.7
Unlike Hetero Secureboost, Homo SecureBoost is conducted under a different setting. In homo SecureBoost, every participant(clients) holds data that shares the same feature space, and jointly train a GBDT model without leaking any data sample.
The figure below shows the overall framework of the homo SecureBoost algorithm.
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Client
Clients are the participants who hold their labeled samples. Samples from all client parties have the same feature space. Clients are to build a more powerful model together without leaking local samples, and they share the same trained model after learning. -
Server
There are potentials of data leakage if all participants send its local histogram(which contains sum of gradient and hessian) to each other because sometimes features and labels can be inferred from gradient sums and hessian sums. Thus, to ensure security in the learning process, the Server uses secure aggregation to aggregate all participants' local histograms in a safe manner. The server can get a global histogram while not getting any local histogram and then find and broadcast the best splits to clients. Server collaborates with all clients in the learning process.
The key steps of learning a Homo SecureBoost model are described below:
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Clients and Server initialize local settings. Clients and Server apply homo feature binning to get binning points for all features and then to pre-process local samples.
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Clients and Server build a decision tree collaboratively:
a. Clients compute local histograms for cur leaf nodes (left nodes or root node)
b. The server applies secure aggregations: every local histogram plus a random number, and these numbers can cancel each other out. By this way server can get the global histogram without knowing any local histograms and data leakage is prevented. Figure below shows how histogram secure aggregations are conducted.
c. The server commit histogram subtractions: getting the right node histograms by subtracting left node local histogram from parent node histogram. Then, the server will find the best splits points and broadcast them to clients.
d. After getting the best split points, clients build the next layer for the current decision tree and re-assign samples. If current decision tree reaches the max depth or stop conditions are fulfilled, stop build the current tree, else go back to step (1). Figure below shows the procedure of fitting a decision tree.
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If tree number reaches the max number, or loss is converged, Homo SecureBoost Fitting process stops.
By following the steps above clients are able to jointly build a GBDT model. Every client can then conduct inference on a new instance locally.
Homo SecureBoost utilizes data parallelization and histogram subtraction to accelerate the learning process.
- Every party use mapPartitions and reduce API interface to generate local feature-histograms, only samples in left nodes are used in computing feature histograms.
- The server aggregates all local histograms to get global histograms then get sibling histograms by subtracting left node histograms from parent histograms.
- The server finds the best splits from merged global histograms, then broadcast best splits.
- The computational cost and transmission cost are halved by using node subtraction.
Homo SecureBoost supports the following applications:
- binary classification, the objective function is sigmoid cross-entropy
- multi classification, the objective function is softmax cross-entropy
- regression, objective function includes least-squared-error-loss, least-absolutely-error-loss, huber-loss, tweedie-loss, fair-loss, log-cosh-loss
- Server uses safe aggregations to aggregate clients' histograms and losses, ensuring data security
- Column sub-sample
- Controlling the number of nodes to split parallelly at each layer by setting max_split_nodes parameter，in order to avoid memory limit exceeding
- Support feature importance calculation
- Support Multi-host and single guest to build model
- Support missing value in train and predict process
- Support evaluate training and validate data during training process
- Support feature subsample random seed setting in FATE-1.5
- Support feature binning error setting.
Now Hetero SecureBoost adds a new option: complete_secure. Once enabled, the boosting model will only use guest features to build the first decision tree. This can avoid label leakages, accord to SecureBoost: A Lossless Federated Learning Framework.
We support Hetero Fast SecureBoost, in abbreviation, fast SBT, in FATE-1.5. The fast SBT uses guest features and host features alternately to build trees, in order to save encryption costs and communication costs. In fast SBT, we support MIX mode and LAYERED mode and they use different strategies while building decision trees.
In mix mode, we offer a new parameter 'tree_num_per_party'. Every participated party will build 'tree_num_per_party' trees using their local features, and this procedure will be repeated until reach the max tree number. Figure 5 illustrates the mix mode.
While building a guest tree, the guest party side simply computes g/h and finds the best split points, other host parties will skip this tree and wait. While building a host tree, the related host party will receive encrypted g/h and find the best split points with the assistant of the guest party. The structures of host trees and split points will be preserved on the host side while leaf weights will be preserved on the guest side. In this way, encryption and communication costs are reduced by half. (If there are two parties)
While conducting inference, every party will traverse its trees locally. All hosts will send the final leaf id to guests and the guest retrieves leaf weights using received leaf id. The prediction only needs one communication in mix mode.
In layered mode, only supports one guest party and one host party. The host will be responsible for building the first "host_depth" layers, with the help of the guest, and the guest will be responsible for the next "guest_depth" layers. All trees will be built in this 'layered' manner.
The benefits of layered mod is obvious, like the mix mode, parts of communication costs and encryption costs will be saved in the process of training. When predicting, we only need one communication because all host can conduct inferences of host layers locally.
According to experiments on our standard data sets, mix mode and layered mode of Fast SBT can still give performances (sometimes even better) equivalent to standard Hetero SecureBoost, even the training data is unbalanced distributed in different parties or contains noise features. (Binary, multi-class, and regression tasks are tested). At the same time, the time consumption of FAST SBT is reduced by 30% ~ 50% on average.
Fast SBT uses guest features and host features alternately by trees/layers to reduce encryption and communication costs.
- Prediction only needs one communication round.
Fast SBT supports the following applications.
- binary classification, the objective function is sigmoid cross-entropy
- multi classification, the objective function is softmax cross-entropy
- regression, objective function includes least-squared-error-loss、least-absolutely-error-loss、huber-loss、tweedie-loss、fair-loss、 log-cosh-loss
- In mix mode, every host parties only keep their own tree models. Guest will only keep guest trees and host leaves.
- In mix mode, host side support feature importance calculation (split type is supported, gain type is not supported)
- In layered mode, model exporting setting is the same as the normal-SBT.
- The time consumption of FAST SBT is reduced by 30% ~ 50% on average.
In the traditional GBDT setting, the strategy of multi-classification learning is to separate the gradient/hessian of each class and learn a tree for each class independently. Each tree is responsible for predicting a single variable. In the vertical federated scenario, using a traditional single-output-tree-based multi-classification strategy has its limitations: All the computation costs and communication overheads are amplified by the times of the number of labels. It will be extremely time-consuming if we are training on a dataset with many types of labels.
To address the efficiency problem, in FATE-1.8, we propose a novel multi-output-tree based vertical boosting tree techniques for multi-classification tasks. Leaves of multi-output-tree give multi-dimension output, corresponding to every class. Instead of learning trees for every class separately, now we only need to fit one tree at every boosting epoch. We also combines our cipher optimization techniques with SBT-MO. According to our preliminary experiments, on the conditions of reaching the same accuracy, SBT-MO reduces tree building time(ignoring data preprocessing and evaluation time) by over 50%+. For more details of SBT-MO, please refer to SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning.
SBT-MO solve multi-classification tasks with multi-output decision trees. Save time when training on a dataset with many types of labels.
- multi classification, the objective function is softmax cross-entropy