Release v0.3.0

Summarization

The improvements and modifications in this release (FederatedScope v0.3.0) are summarized as follows:

• Tree-based Models. FederatedScope allows users to train tree-based models in vertical federated learning (VFL). We provide the implementations of several widely-used models (such as XGBoost, GBDT, RandomForest, etc.) and dataloaders of benchmark datasets. For different kinds of tree-based models in VFL, users can apply different protection mechanisms (such as DP, OP_Boost, HE, etc.) to adjust the strength of privacy protection accordingly. Note that these modules are also built with event-driven architecture to support both convenient usage and flexible customizations. For more details, please refer to federatedscope/vertical_fl).
• Efficiency and Effectiveness. We provide several advanced functionalities to improve both efficiency and effectiveness of computation and communication in FL algorithms, including training parallelization (at federatedscope/core/parallel), message compression (at federatedscope/core/ compression), and robust aggregators (at federatedscope/core/aggregators). These provided functionalities can be useful to promote federated learning in both academic research and real-world applications.
• Attack and Defense. We provide a range of defense strategies against adversarial attacks, including Krum, Multi-Krum, Median, NormBounding, Bulyan, and Simple Tunning. In addition, we will be releasing a benchmark for backdoor attack and defense on personalization FL, which allows users to test various data poisoning-based backdoor attacks such as BadNet, Blend, SIG, and edge-case.
• Personalization FL. We leverage FederatedScope to establish a comprehensive benchmark for personalized Federated Learning (accepted by NeurIPS'22). During the process, a large number of personalized algorithm implementations have been improved and validated. Some new personalized FL algorithms are also included. We welcome more contributions and feedback for the research and applications of personalized FL!
• More Exploration and Materials. We continue exploring and developing new algorithms in a wide range of FL applications and research topics, including hyperparameter optimization, graph learning, NLP, fairness, and so on. The materials (such as paper lists) of these promising topics are constantly being updated, please feel free to contribute!

FederatedScope aims to provide both easy-to-use functionalities and flexible development interfaces for users. We sincerely hope that FederatedScope can help users and developers in building new FL applications and proposing new Fl algorithms, and greatly welcome the community to contribute via discussions, suggestions, commitments, and other participations.
Thank you very much for your interest!

Commits

Features & Enhancements

• [Tree] Dev label-based tree model for VFL @qbc2016 @xieyxclack (#568, #559, #554, #528)
• Add FedRep and Simple_tuning @Alan-Qin (#564)
• Load data from files @xieyxclack (#558)
• Add compression methods @xieyxclack (#555)
• Add several Byzantine robust algorithms @private-mechanism (#552)
• [Tree] Support distributed mode for VFL @qbc2016 (#553, #476, #471)
• [Tree] Add xgb evaluation @qbc2016 (#549, #434)
• Optimize MF model @rayrayraykk (#544)
• [Tree] Dev feature-based tree models for VFL @xieyxclack @qbc2016 (#533, #530, #529, #523, #510, #497, #482, #476, #439, #427)
• Add FedSGLD Exp @joneswong (#520)
• [HPO] Add SWA @rayrayraykk (#519)
• Add new defense algo and sampler @xieyxclack (#512)
• [HPO] pfedhpo and fts @TheSunWillRise (#509)
• Add GitHub Actions of CI and format check @rayrayraykk (#508, #414)
• Parallelization for standalone mode with NCCL @pan-x-c (#487)
• [Tree] Add feedback during training of xgb @qbc2016 (#484, #438)
• [HPO] Enabled personalized policy for fedex @joneswong (#481)
• Enabled to minimize local entropy @joneswong (#468)
• Add data, model, and algo that FedSAM needs @joneswong (#453)
• [NLP] Initialization for hetero tasks in NLP @xieyxclack (#449)
• Add condition param to autotune; Add feature engineering module @rayrayraykk (#426)
• Add dataset for vertical_fl @qbc2016 (#423)
• Refactor FedRunner, optimize trainer module and optimize CI @rayrayraykk (#415)
• Enhance client_cfg and add new dataset @rayrayraykk (#413)
• [NLP] Development for fl-nlp-hetero-tasks @cheneydon (#410)
• Add more registers & refactor splitter @rayrayraykk @xieyxclack (#466, #394, #372)
• Add more fairness metrics @cuiyuebing (#392)
• Add checks for completeness of msg_handler @rayrayraykk (#388)
• [HPO] Add HPOBench as backend demo for FedHPOBench @rayrayraykk (#474, #381, #377)
• Refactor data-related interfaces & add interfaces for trainer and worker @rayrayraykk (#365)
• Add color logging and move logging related utils to logging @rayrayraykk (#355)
• FedGlobalContrast and FedSimCLR baseline @xkxxfyf (#354)
• Add parameters to control whether to check cfg @rayrayraykk (#351)
• [HPO] Support fairness related vector value in FedHPOB @rayrayraykk (#348)
• [HPO] Add hyperband and randomsearch from Hyperbandster @rayrayraykk (#343)
• Add utils for draw landscape @rayrayraykk (#338)
• [pFL] Remove redundant eval hook for pFedMe @yxdyc (#337)
• Add new system model @rayrayraykk (#336)
• Membership inference attack: add comparison when target data is in/not in the training batch @Osier-Yi (#335)
• [HPO] Add grid search and twitter to FedHPO-B @rayrayraykk (#324, #320)
• Make the message comparator more robust @yxdyc (#314)
• [HPO] Add BO_GP and BO_RF @rayrayraykk (#311)
• [HPO] Enable wrap hpbandster for FedEx @rayrayraykk (#308)
• [HPO] Apply the updates according to the exp of fedhpo-b @joneswong (#307)
• Support using cached data and re-splitting for huggingface datasets @yxdyc (#302)
• Re-organize aggregators @xieyxclack (#299)
• Support help and required argument for the configs @yxdyc (#294)
• Add cross-device recsys dataset Netflix @rayrayraykk (#281)

Bug fixes

• Bug fix for nbafl @DavdGao (#566)
• Bugfix for vfl algos and datasets @xieyxclack @qbc2016 (#550, #548, #537, #506, #475, #436)
• Minor fixes for cfg, scripts, docs, and others @xieyxclack @rayrayraykk @Osier-Yi @qbc2016 (#547, #541, #546, #522, #513, #485, #461, #442, #437, #424, #405, #387, #352, #345, #323, #319, #318, #315, #306)
• Fix python version in dockerfile @rayrayraykk (#536)
• Fix bug in transfomer_builder @rayrayraykk (#515)
• Change default value of msg.content to 'None' @xieyxclack (#496)
• Install libxml-parser-perl in test_atc @xieyxclack (#495)
• Modify the import source of update_logger @private-mechanism (#491)
• Fix roc_auc @rayrayraykk (#467)
• Fix the state of message for evaluation @xieyxclack (#470)
• Fix b-local dissim @rayrayraykk (#463)
• Fix torch trainer example @rayrayraykk (#451)
• Set pin_memory to False to avoid OOM @rayrayraykk (#444)
• Fix rmse metric @DavdGao (#378)
• Fix early_stop when the metric is the larger the better @rayrayraykk (#374)
• Bugfix for merge_data @yxdyc (#385)
• Bugfix for yaml dump due to the Argument class @yxdyc (#358)
• Fix one-shot exp utils @rayrayraykk (#317)
• Fix client state error @joneswong (#291)
• Fix twitter related bugs and merge_test_data @rayrayraykk (#284)
• Fix call_link_level_trainer() and call_node_level_trainer() @ahn1340 (#274)

Documents & Materials

• Update news and README @xieyxclack @rayrayraykk (#569, #563, #526, #521, #402, #395, #361, #326, #295)
• Update README for tree-based models @qbc2016 (#567, #556)
• Update paper list: attack, fairness, incentive @Osier-Yi (#565, #407)
• Update docs of script and configs @joneswong @yxdyc @DavdGao @Osier-Yi @xieyxclack @rayrayraykk @qbc2016 (#562, #380, #347 #325, #316, #305, #303, #301, #300, #297, #296, #293, #292, #287, #286, #285, #282)
• Update paper list for pFL @yxdyc (#561, #421)
• Update paper list for FL-NLP @cheneydon (#560, #504, #419, #283)
• Update paper list for untargeted attacks in FL @private-mechanism (#507)
• Update paper list for FL-Tree @qbc2016 (#493, #422, #401)
• Update paper list for FL-Rec @xieyxclack (#425)
• Update gfl paper @rayrayraykk @joneswong (#420, #418, #350)
• Update README.md for installation @rayrayraykk (#417)
• Add paper list for self-supervision, multi-task and medical data on federated learning @xkxxfyf (#375)
• Add KDD'22 tutorial material and news icon @xieyxclack (#362)
• Supplement copyright of GraphGym @joneswong (#333)
• Add scripts for FedProx on Cora @rayrayraykk (#331)
• Doc scripts pfl @yxdyc (#328)
• Update notebook tutorials @rayrayraykk (#322)
• Add scripts for running gcn with dp @rayrayraykk (#313)
• Add README for FS-G @rayrayraykk (#279)