Federated Learning using Hugging Face and Flower

Back to Articles Federated Learning using Hugging Face and Flower Published March 27, 2023 Update on GitHub Upvote 2 Charles Beauville charlesbvll Follow guest Dependencies Standard Hugging Face workflow Handling the data Training and testing the model Creating the model itself Federating the...

Back to Articles Federated Learning using Hugging Face and Flower Published March 27, 2023 Update on GitHub Upvote 2 Charles Beauville charlesbvll Follow guest Dependencies Standard Hugging Face workflow Handling the data Training and testing the model Creating the model itself Federating the example Creating the IMDBClient Starting the server Putting everything together This tutorial will show how to leverage Hugging Face to federate the training of language models over multiple clients using Flower. More specifically, we will fine-tune a pre-trained Transformer model (distilBERT) for sequence classification over a dataset of IMDB ratings. The end goal is to detect if a movie rating is positive or negative. A notebook is also available here but instead of running on multiple separate clients it utilizes the simulation functionality of Flower (using flwr['simulation']) in order to emulate a federated setting inside Google Colab (this also means that instead of calling start_server we will call start_simulation, and that a few other modifications are needed). Dependencies To follow along this tutorial you will need to install the following packages: datasets, evaluate, flwr, torch, and transformers. This can be done using pip: pip install datasets evaluate flwr torch transformers Standard Hugging Face workflow Handling the data To fetch the IMDB dataset, we will use Hugging Face's datasets library. We then need to tokenize the data and create PyTorch dataloaders, this is all done in the load_data function: import random import torch from datasets import load_dataset from torch.utils.data import DataLoader from transformers import AutoTokenizer, DataCollatorWithPadding DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") CHECKPOINT = "distilbert-base-uncased" def load_data(): """Load IMDB data (training and eval)""" raw_datasets = load_dataset("imdb") raw_datasets = raw_datasets.shuffle(seed=42) # remove unnecessary data split del raw_datasets["unsupervised"] tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT) def tokenize_function(examples): return tokenizer(examples["text"], truncation=True) # We will take a small sample in order to reduce the compute time, this is optional train_population = random.sample(range(len(raw_datasets["train"])), 100) test_population = random.sample(range(len(raw_datasets["test"])), 100) tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) tokenized_datasets["train"] = tokenized_datasets["train"].select(train_population) tokenized_datasets["test"] = tokenized_datasets["test"].select(test_population) tokenized_datasets = tokenized_datasets.remove_columns("text") tokenized_datasets = tokenized_datasets.rename_column("label", "labels") data_collator = DataCollatorWithPadding(tokenizer=tokenizer) trainloader = DataLoader( tokenized_datasets["train"], shuffle=True, batch_size=32, collate_fn=data_collator, ) testloader = DataLoader( tokenized_datasets["test"], batch_size=32, collate_fn=data_collator ) return trainloader, testloader trainloader, testloader = load_data() Training and testing the model Once we have a way of creating our trainloader and testloader, we can take care of the training and testing. This is very similar to any PyTorch training or testing loop: from evaluate import load as load_metric from transformers import AdamW def train(net, trainloader, epochs): optimizer = AdamW(net.parameters(), lr=5e-5) net.train() for _ in range(epochs): for batch in trainloader: batch = {k: v.to(DEVICE) for k, v in batch.items()} outputs = net(**batch) loss = outputs.loss loss.backward() optimizer.step() optimizer.zero_grad() def test(net, testloader): metric = load_metric("accuracy") loss = 0 net.eval() for batch in testloader: batch = {k: v.to(DEVICE) for k, v in batch.items()} with torch.no_grad(): outputs = net(**batch) logits = outputs.logits loss += outputs.loss.item() predictions = torch.argmax(logits, dim=-1) metric.add_batch(predictions=predictions, references=batch["labels"]) loss /= len(testloader.dataset) accuracy = metric.compute()["accuracy"] return loss, accuracy Creating the model itself To create the model itself, we will just load the pre-trained distillBERT model using Hugging Face’s AutoModelForSequenceClassification : from transformers import AutoModelForSequenceClassification net = AutoModelForSequenceClassification.from_pretrained( CHECKPOINT, num_labels=2 ).to(DEVICE) Federating the example The idea behind Federated Learning is to train a model between multiple clients and a server without having to share any data. This is done by letting each client train the model locally on its data and send its parameters back to the server, which then aggregates all the clients’ parameters together using a predefined strategy. This process is made very simple by using the Flower framework. If you want a more complete overview, be sure to check out this guide: What is Federated Learning? Creating the IMDBClient To federate our example to multiple clients, we first need to write our Flower client class (inheriting from flwr.client.NumPyClient). This is very easy, as our model is a standard PyTorch model: from collections import OrderedDict import flwr as fl class IMDBClient(fl.client.NumPyClient): def get_parameters(self, config): return [val.cpu().numpy() for _, val in net.state_dict().items()] def set_parameters(self, parameters): params_dict = zip(net.state_dict().keys(), parameters) state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) net.load_state_dict(state_dict, strict=True) def fit(self, parameters, config): self.set_parameters(parameters) print("Training Started...") train(net, trainloader, epochs=1) print("Training Finished.") return self.get_parameters(config={}), len(trainloader), {} def evaluate(self, parameters, config): self.set_parameters(parameters) loss, accuracy = test(net, testloader) return float(loss), len(testloader), {"accuracy": float(accuracy)} The get_parameters function lets the server get the client's parameters. Inversely, the set_parameters function allows the server to send its parameters to the client. Finally, the fit function trains the model locally for the client, and the evaluate function tests the model locally and returns the relevant metrics. We can now start client instances using: fl.client.start_numpy_client( server_address="127.0.0.1:8080", client=IMDBClient(), ) Starting the server Now that we have a way to instantiate clients, we need to create our server in order to aggregate the results. Using Flower, this can be done very easily by first choosing a strategy (here, we are using FedAvg, which will define the global weights as the average of all the clients' weights at each round) and then using the flwr.server.start_server function: def weighted_average(metrics): accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics] losses = [num_examples * m["loss"] for num_examples, m in metrics] examples = [num_examples for num_examples, _ in metrics] return {"accuracy": sum(accuracies) / sum(examples), "loss": sum(losses) / sum(examples)} # Define strategy strategy = fl.server.strategy.FedAvg( fraction_fit=1.0, fraction_evaluate=1.0, evaluate_metrics_aggregation_fn=weighted_average, ) # Start server fl.server.start_server( server_address="0.0.0.0:8080", config=fl.server.ServerConfig(num_rounds=3), strategy=strategy, ) The weighted_average function is there to provide a way to aggregate the metrics distributed amongst the clients (basically this allows us to display a nice average accuracy and loss for every round). Putting everything together If you want to check out everything put together, you should check out the code example we wrote for the Flower repo: https://github.com/adap/flower/tree/main/examples/quickstart-huggingface. Of course, this is a very basic example, and a lot can be added or modified, it was just to showcase how simply we could federate a Hugging Face workflow using Flower. Note that in this example we used PyTorch, but we could have very well used TensorFlow. More Articles from our Blog peftloraguide Beyond LoRA: Can you beat the most popular fine-tuning technique? 75 June 18, 2026 cvfederated-learningfl Creating Privacy Preserving AI with Substra 3 April 12, 2023 Community BlackBytex Feb 13, 2025 nice try Reply EditPreview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here. Tap or paste here to upload images Comment · Sign up or log in to comment Upvote 2

Source: Hugging Face — Published March 27, 2023 at 00:00 UTC — Category: Models