Distributed Systems & Streaming NLP (2024-2025)
Collection
Real-time Kafka/Spark streaming sentiment classification and privacy-preserving multi-bank federated learning using Flower. • 2 items • Updated
Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
person_age: double
person_gender: int64
person_income: double
person_emp_exp: int64
person_home_ownership: int64
loan_amnt: double
loan_int_rate: double
loan_percent_income: double
cb_person_cred_hist_length: double
credit_score: int64
previous_loan_defaults_on_file: int64
loan_status: int64
federated_node: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1979
to
{'federated_node': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2406, in _iter_arrow
pa_table = cast_table_to_features(pa_table, self.features)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2280, in cast_table_to_features
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
person_age: double
person_gender: int64
person_income: double
person_emp_exp: int64
person_home_ownership: int64
loan_amnt: double
loan_int_rate: double
loan_percent_income: double
cb_person_cred_hist_length: double
credit_score: int64
previous_loan_defaults_on_file: int64
loan_status: int64
federated_node: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1979
to
{'federated_node': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:The task_ids "binary-classification" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
🔒 Federated Learning Multi-Bank Loan Approval Benchmark
🤝 Engineering Project Team: Oussama EL HADJI • Abdellatif CHAKOR
A privacy-preserving financial AI benchmark dataset partitioned across 3 distributed banking institutions (bank1, bank2, bank3) totaling 30,000 credit records.
👥 Project Engineering Team
| Engineer | Project Role & Contributions | Profiles |
|---|---|---|
| Oussama EL HADJI | Federated Aggregation & Evaluation Lead | Flower (flwr) Server Architecture, FedAvg Simulation, Convergence Analysis |
| Abdellatif CHAKOR | Distributed Client & Data Partitioning Engineer | Non-IID Banking Data Partitioning, Local Client Training Nodes, Kafka Integration |
🏛️ Partition Topology
┌────────────────────────┐
│ Central Server (FedAvg)│
│ Apache Kafka Broker │
└───────────┬────────────┘
┌────────────────────┼────────────────────┐
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Bank 1 (Node A) │ │ Bank 2 (Node B) │ │ Bank 3 (Node C) │
│ 10,000 records │ │ 10,000 records │ │ 10,000 records │
│ Urban Retail │ │ Commercial SME │ │ Rural Agriculture
└─────────────────┘ └─────────────────┘ └─────────────────┘
📊 Federated Averaging Benchmark Results
| Model Architecture | Partition Setting | Test Accuracy | F1-Score | Privacy Guarantee |
|---|---|---|---|---|
| Local Baseline (Isolated) | Bank 1 only | 81.2% | 0.79 | Node-level only |
| Centralized Upper Bound | Pooled (Violates Privacy) | 91.8% | 0.90 | None (Data shared) |
| Federated FedAvg (Flower) | Non-IID 3 Nodes | 89.6% | 0.88 | Zero raw-data transfer |
📖 Citation
@dataset{elhadji_chakor_2025_federated,
author = {El Hadji, Oussama and Chakor, Abdellatif},
title = {Multi-Institution Non-IID Credit Risk Benchmark for Federated Learning},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/bosaj/federated-loan-approval-benchmark}}
}
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