CHANGELOG.md

Fed-BioMed changelog

2023-06-23 version 4.4.1

• fix secure aggregation vector encoding bug

2023-06-05 version 4.4.0

• add HTTPS secure access to Fed-BioMed Node GUI
• introduce GitHub workflow/actions for CI build tests and testing/publishing documentation.
• introduce versioning for component config files, MQTT messages and breakpoints
• migrate to GitHub
• migrate docs source into main repository and point to https://fedbiomed.org
• fix robustness of handling secure aggregation training errors.
• fix warnings in TorchModel.set_weights and BatchNorm layers' handling
• fix incorrect calculation of SkLearn model weights
• fix incorrect compatibility of FedProx feature with Model class
• fix ordering of weights and node replies after training.

2023-04-26 version 4.3

• introduce secure aggregation using Joye-Libert scheme and Shamir MPC key computation
• update MONAI and scikit-learn version used
• fix Scaffold incorrectly applying correction states
• fix incorrect Perceptron default values for scikit-learn models
• fix Experiment.set_training_args() not propagating updated value
• fix environment cleaning to handle configuration file content change
• fix docker wrapping scripts to restrict container account names to alphanumeric characters
• misc improve node CLI for non-interactive add of MedicalFolderDataset using a json file

2023-02-08 version 4.2

• add support for docker compose v.2 file syntax
• fix model weights computation occurring during aggregation, by sending dataset sample size from node to researcher
• fix GUI regression failure, after merging MP-SPDZ certificate generation - such issue was freezing some web browsers
• fix incoherent tag handling: make explicit the way datasets are tagged on nodes
• fix unit tests failure, when launched from root directory, due to missing mocking facility
• fix fedbiomed_run error: prevent launching researcher when no config file exists
• misc improve make sure only one dataset per Node is selected during the training
• misc remove uncorrect warning about optimizer_args when using SKlearn training plan

2023-01-05 version 4.1

• introduce Scaffold implementation for PyTorch
• introduce training based on iteration number (num_updates) as an alternative to epochs
• introduce provisions for including external contributors in the project
• add nightly continuous integration test of selected notebooks
• add documentations for network matrix and security model
• update image segmentation notebook to match documentation tutorial
• add round number in researcher side training progress message
• fix MedicalFolderDataset with demographics file using column 0 as subject folder key
• fix batch size display when using Opacus
• fix loss display when using FedProx
• fix default value of batch_maxnum to a reasonable value of 0
• fix validation with custom metrics backend code and validation example notebook
• fix image segmentation notebook typo
• misc improve MedicalFolderDataset with a reasonable default value for demographics_transform
• misc improve error message when dataset geometry does not meet researcher side quality check

2022-11-17 version 4.0

• introduce IXI (image + CSV file) dataset support as MedicalFolderDataset
• add advanced brain image segmentation tutorial for IXI dataset
• add node side GUI support for IXI dataset
• major redesign of training plan implementation for genericity
• redesign Opacus integration with torch training plan
• implement central and local differential privacy (CDP/LDP) for Pytorch training plan
• introduce integration with FLamby FL benchmark package
• introduce node side GUI user accounts, authentication, accounts management
• introduce data loading plan functionality for dataset load-time custom view on the node side
• add data loading plan support for IXI medical folder dataset
• introduce training plan approval capability in application: researcher request, node approval CLI
• add node side GUI support for training plan approval
• introduce mini-batch support in scikit-learn training plans
• refactor scikit-learn training plans with hierarchical design
• refactor NIFTI folder dataset type for code quality and robustness
• TrainingArgs class to manage/verify training arguments on researcher side
• rename model approval as training plan approval for coherency
• add sample notebook for researcher-side filtering of datasets on minimum samples number
• obfuscate node side path to researcher for better privacy
• misc node side TinyDB database access refactor for code quality and robustness
• misc improve scikit-learn training plan dependency handling
• fix bug on training plan report of sample/percentage progress
• fix missing fedprox_mu parameter in training args
• fix dry run mode for pytorch training plan
• fix conda environment GLIBC version issue

2022-05-09 version 3.5

• add FedProx optimization scheme support for PyTorch in training plan
• data manager to provide robust solution on managing node datasets before training on the node side
• model evaluation/validation on the node side over locally updated and aggregated model parameters
• add NIFTI folder dataset type
• add option to load MedNIST dataset in node
• update docstrings for API documentation support
• node container support for GPU with PyTorch, tested on Fedora 35
• debug and robustify VPN/containers environment, test on Ubuntu 20 and Windows 10 + WSL2
• VPN/containers environment scripts for simpler management of containers and peers
• refactor training_plans/_fedbiosklearn.py to get rid off eval()
• change training_plans file organisation
• create a top class for training_plans
• removed the magic %writefile use in notebooks to save the user's defined model
• more unittests and flake8 parsing
• add validation class for checking user input

2022-02-25 version 3.4

• rewriting of Opacus notebook
• new tutorial notebook on Experiment() usage
• add .coveragerc to tune test coverage
• fix mqtt logger loop then mqqt not reachable
• replace @property getters/setters by proper getters() setters(), still in progress
• a lot of new unit tests and increase test coverage
• refactor of Message() class, simplication of Messages description (purely declarative now)
• add more ErrorNumbers + associated messages
• check user input (mainly in Experiment() for now)
• rename Exceptions as Errors, add FedbiomedError as top class of our errors
• use try/except block at low level layers
• Environ() class refactoring, environment tests rewriting
• Experiment() class refactoring, new API, more setters/getters, interactive use, rename rounds -> round_limit,...
• add single GPU training support for PyTorch
• add a gui to manage data on nodes
• update of sklearn sgdregressor notebook
• update of monai notebook
• Tensorboard fixes for multi class classification with scikit learn

2022-01-07 version 3.3

• add MONAI support and example notebooks
• add model manager to register and check authorized training models on the node based on model hash
• refactor experiment real time monitoring capacity with Tensorboard
• add Request.list() to list shared dataset on online nodes
• configure_conda may take parameters to only update some environments
• fix conda environments for mac OSX
• add -n (dryrun) option for configure_conda, for debug/validation purpose
• fix and refactor breakpoint feature which was not fully operational
• change the names of breakpoint directories
• node error reporting to researcher
• basic error handling on researcher component
• mutualize the Singleton metaclass
• refactor environ as singleton class
• fix the way the tests deal with fedbiomed.common.environ
• refactor strategy (moved some methods in upper classes)
• add command run_integration_test to easily run an integration test from a single .py or .ipynb
• add an automatized method to add a dataset in nodes's db from a JSON dataset description file
• add error numbering as an enum, impact on error messages serialization
• more example notebooks, update existing notebooks
• more unittests
• normalize naming : use term 'node' not 'client'

2021-10-21 version 3.2

• add support for scikit-learn with SGD regressor and example notebook
• add VPN + docker environment for deploying over an untrusted network
• add message logging capability including sending node messages to researcher
• add loss report from node during training and view in tensorboard in researcher
• add save/load state capability after each round during a training
• add capability for listing datasets on each node
• add example notebooks for Celeba and used cars dataset
• WIP add unit tests
• add support for multiple Experiment(), including re-executing a notebook
• fix issue erratic failure when training with 3+ nodes
• test and document Windows 10 installation in WSL

2021-08-13 version 3.1

• merge 3 gitlab repos fedbiomed-{network,node,researcher} in a unique fedbiomed repo
• add new model variational autoencoder (VAE)
• add support for generic dataloader and handling of .csv dataset
• measure execution time on nodes
• WIP adding unit tests
• misc code cleaning

2021-07-05 version 3.0

• initial release of re-implementation based on pytorch model file transfer and MQTT messaging