Enable federated XGBoost using bootstrap aggregation in Task Runner

[kta-intel]

This PR enables a TaskRunner-based federated XGBoost using the bootstrap aggregation

Specifically this PR:

• creates an xgb_higgs task runner workspace to train on the higgs dataset [ref] with all required code (i.e. src/taskrunner.py, src/dataloader.py ,plan/*.yaml, etc.
• adds a tasks_xgb.yaml to enable new FedBaggingXGBoost aggregation when running xgb training workloads
• adds delta_updates parameter to Aggregator in order to bypass delta updating (for deep learning models getting weight deltas makes sense since the model size should stay relatively consistent, for tree-based algorithms, this makes less sense because more trees are added over time)
  • delta_updates is set to true by default to preserve normal behavior. xgboost taskrunner explicitly sets it to false to bypass it
• introduces new loader_xgb.py as the backend / superclass to src/dataloader.py
• introduces new runner_xgb.py as the backend / superclass to src/taskrunner.py
• introduces new federated boostrap algorithm for xgboost in aggregation_function.fed_bagging which bags the latest trees to a global model, consistent with currently accept federated xgboost algorithms in the industry