Federated learning keeps data on clients, but incorrect labels can affect the shared model. FedLN estimates client noise and offers three places to intervene.
A frozen encoder finds other local examples with similar image features. A vote among their stored labels corrects the sample’s label.
AKD · during training
For clients with detected noise, an embedding teacher guides local features alongside the original-label signal. The labels stay unchanged.
NA-FedAvg · at the server
After ordinary warm-up, clients send global- and local-model confidence scores once. The server estimates each client’s noise and reuses it to weight later updates by data share times estimated reliability.
Choose an intervention to fit the data and device.
Label patterns, pretrained representations and available computation determine which route is useful.
Abstract
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users’ devices, in reality, label noise can naturally occur in FL and is closely related to clients’ characteristics. Due to scarcity of available data and significant label noise variations among clients in FL, existing state-of-the-art centralized approaches exhibit unsatisfactory performance, whereas prior FL studies rely on excessive on-device computational schemes or additional clean data available on the server. We propose FedLN, a framework to deal with label noise across different FL training stages, namely FL initialization, on-device model training, and server model aggregation, able to accommodate the diverse computational capabilities of devices in an FL system. Specifically, FedLN computes per-client noise level estimation in a single federated round and improves the models’ performance by either correcting or mitigating the effect of noisy samples. Our evaluation on various publicly available vision and audio datasets demonstrates a 22% improvement on average compared to other existing methods for a label noise level of 60%. We further validate the efficiency of FedLN in human-annotated real-world noisy datasets and report a 4.8% increase on average in models’ recognition performance, highlighting that FedLN can be useful for improving FL services provided to everyday users.
A pretrained encoder maps each client’s examples into an embedding space. Nearby examples vote on the label. When that vote disagrees with the stored label, NNC replaces the label before federated training.
Examples and embeddings stay fixed. The fraction of disagreements also supplies a local noise estimate.
Count one vote per neighbor; keep the most frequent class.
g
Pretrained feature encoder.
Nk
Indices of the k nearest other local embeddings by Euclidean distance.
yj
The neighbor’s observed label; tied votes are broken randomly.
Useful embeddings help, but a neighborhood dominated by the wrong class can still vote incorrectly.
Noise estimation and source
The encoder is used locally during initialization. The per-client noise estimate is the number of vote–label mismatches divided by the client’s sample count. The displayed voting rule is a compact restatement, with repeated labels counted separately.
AKD keeps the observed labels and adds a teacher loss for clients with detected label noise. The teacher supplies pretrained embeddings or predictions from the aggregated global model.
The local model learns from its labels and this additional supervision in one combined objective.
Add teacher guidance when the client’s noise estimate activates it.
CE / KD
Cross-entropy label loss / knowledge-distillation teacher loss.
nlm, uε
Client m’s estimated noise and its threshold-based gate.
β
Controls the teacher loss’s contribution.
The teacher is extra supervision; it does not force a potentially incorrect replacement label.
Teacher losses and source
Embedding supervision uses mean absolute error. Global-model supervision uses Kullback–Leibler divergence between temperature-scaled outputs. The reported results on this page use embedding supervision.
After a warm-up period, clients compute energy scores using their local and global models. The server uses the scores to estimate each client’s label-noise level once.
Later rounds reuse those estimates: a client’s data share is multiplied by its estimated clean-label fraction.
Noisier clients contribute less to the shared model.
γm
Client m’s aggregation weight.
Nm / N
Its share of all training samples.
1 − nlm
Its estimated fraction of clean labels.
This avoids a separate pretrained encoder, but still depends on distinguishing relatively reliable clients.
One-time estimation and source
Energy scores come from the model’s output logits. Score computation and communication occur in the designated estimation round; subsequent rounds continue local training and noise-aware aggregation. The equation reproduces the paper’s weight without adding a normalization term.
Choose one route. Each highlighted step shows where that method intervenes.
Before trainingEvery round · send shared model → train locally → aggregate ↻
NNC
Correct labels
Initialization
Neighbor vote → corrected labels
Encode local examples; replace each label with its neighbors’ majority vote.
Local training
Train on corrected labels
Minimize cross-entropy on each participating client.
Server aggregation
Sample-weighted FedAvg
Average client models by their share of training samples.
AKD
Guide local learning
Initialization
Estimate client noise
Count disagreements between local labels and embedding-based votes.
Local training
Label loss + teacher guidance
Keep observed labels; use estimated noise to gate the extra teacher loss.
Server aggregation
Sample-weighted FedAvg
Average client models by their share of training samples.
NA-FedAvg
Reweight updates
Initialization
Initialize the shared model
Start ordinary FedAvg, without an encoder or label correction.
Local training
Train on observed labels
At the estimation round, also send local/global-model energy scores.
Server aggregation
Data share × clean fraction
Estimate client noise once after warm-up; reuse its clean fraction (1 − noise) for aggregation.
Each round starts from the shared model; client data stay local.Algorithm 1
Evaluation
Results
Learning with human label noise
Test accuracy (%) · 3-run means · AKD uses embeddings
CIFAR-10N ↑ Higher is better
050100
FedAvg
CIFAR-10N (%): 65.68
CL
CIFAR-10N (%): 67.31
FedCorr
CIFAR-10N (%): 70.02
NNC
CIFAR-10N (%): 74.69
AKD
CIFAR-10N (%): 70.39
NA-FedAvg
CIFAR-10N (%): 70.06
CIFAR-100N ↑ Higher is better
050100
FedAvg
CIFAR-100N (%): 47.25
CL
CIFAR-100N (%): 48.72
FedCorr
CIFAR-100N (%): 54.12
NNC
CIFAR-100N (%): 55.39
AKD
CIFAR-100N (%): 57.39
NA-FedAvg
CIFAR-100N (%): 53.99
Noise patterns change the outcome
CIFAR-10 · 70% label noise on 80% of clients · Accuracy (%)
When label correction helps—and fails. CIFAR-10, ResNet-20; 70% label noise on 80% of 30 clients; 200 rounds, one local epoch, 80% participation. Test accuracy (%), higher is better; mean of three runs, no dispersion reported.
@article{tsouvalas2024fedln,
author = {Tsouvalas, Vasileios and Saeed, Aaqib and Ozcelebi, Tanir and Meratnia, Nirvana},
title = {Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels},
journal = {ACM Transactions on Intelligent Systems and Technology},
year = {2024},
volume = {15},
number = {2},
articleno = {22},
numpages = {26},
doi = {10.1145/3626242},
url = {https://doi.org/10.1145/3626242}
}