A federated minimax framework for heterogeneous clients.
problem Training with edge devices having different datasets and capabilities.
method Proposes a federated minimax optimization framework with normalized updates.
result Improves convergence and communication complexity for nonconvex functions.
New methods handle both data and network heterogeneity in federated learning.
problem Challenges in federated learning due to data and network heterogeneity.
method Two novel client selection schemes that minimize theoretical runtime to convergence.
result Our methods are at least competitive to and up to 20 times better than existing baselines.
HeteroFL trains diverse clients with varying capabilities efficiently.
problem Training models on clients with different computational and communication capacities.
method Proposes HeteroFL framework to adaptively distribute subnetworks based on client capabilities.
result Adaptive subnetwork distribution leads to efficient computation and communication.
A federated model learns shared archetypes from heterogeneous clients in continual learning.
problem Federated learning struggles with client heterogeneity and streaming distribution shifts.
method Clients encode their data as low-rank Hebbian operators, which are sent to a central server for aggregation and factorization into global archetypes.
result Improved global archetype reconstruction and associative retrieval in heterogeneous clients, drift, and novelty settings.
A new algorithm optimizes local objectives in federated learning with heterogeneous clients.
problem Optimizing local objectives in federated learning with heterogeneous client data.
method Proposes PF-PNE algorithm with double elimination strategy.
result PF-PNE algorithm optimizes local objectives with arbitrary heterogeneity and protects client data confidentiality.
Study shows data heterogeneity affects distributed learning's generalization error.
problem Effect of data heterogeneity on distributed learning performance.
method Established bounds on generalization error using information-theoretic rate-distortion theory.
result Data heterogeneity improves generalization error for distributed learning.
Group personalization improves FL performance in heterogeneous client data.
problem Mitigating client drift in federated learning with heterogeneous data.
method Fine-tuning a global FL model over homogeneous groups of clients, then personalizing each group's model.
result The proposed method achieves superior personalization performance compared to other FL approaches.
Study on incentivizing truthfulness in federated learning with heterogeneous data.
problem Manipulated updates in federated learning due to data heterogeneity.
method Formulated a game-theoretic approach to prevent clients from misreporting their gradient updates.
result Developed a payment rule that provably disincentivizes sending modified updates in federated learning.
A framework for federated learning with heterogeneous data.
problem Federated learning with data from clients using different data representations.
method FLIC framework that maps client data into a common feature space via local embedding functions, learned federally using Wasserstein barycenters and trained locally via distribution alignment.
result FLIC outperforms FL benchmarks with heterogeneous input feature spaces.
This paper tackles objective inconsistency in federated optimization with heterogeneous clients.
problem Objective inconsistency due to heterogeneity in clients' datasets and computation speeds.
method General framework for analyzing federated heterogeneous optimization algorithms, including FedAvg and FedProx, and proposing FedNova.
result FedNova eliminates objective inconsistency while preserving fast error convergence.
FedGLOMO accelerates FL convergence for non-convex functions.
problem Efficiently solving non-convex optimization problems in federated learning with client heterogeneity.
method Combines global and local momentum updates to reduce variance and improve convergence rate.
result Achieves O(ε−1.5) convergence to ε-stationary point, compared to O(ε−2). A new FL algorithm reduces communication overhead by selectively updating model parameters.
problem Data heterogeneity and communication overhead in federated learning.
method Uses age of information metric to selectively update model parameters and group clients with similar data.
result Our method can expedite training and surpass other communication-efficient strategies in efficiency.
Proposes a method for private aggregation in heterogeneous federated learning.
problem Ensuring resilience to Byzantine clients and maintaining client data privacy in federated learning with heterogeneous data.
method Careful co-design of verifiable secret sharing, secure aggregation, and private information retrieval scheme.
result Achieves information-theoretic privacy guarantees and Byzantine resilience under data heterogeneity.
This paper improves federated learning for industrial predictive analytics by accommodating client heterogeneity.
problem Traditional federated models assume homogeneity in degradation processes, which doesn't apply to industrial settings.
method Personalized federated prognostic model using proximal gradient descent algorithm for joint parameter estimation.
result The proposed model enhances performance and provides comprehensive failure time distributions.
FedAMD framework improves federated learning with partial client participation.
problem Data heterogeneity and inactive client updates in partial client participation.
method Anchor sampling divides clients into anchor and miner groups, using large and small batches respectively.
result FedAMD achieves faster convergence and improved model performance compared to state-of-the-art methods.
Study on federated learning with private label sets, showing privacy benefits without significant accuracy loss.
problem Effects of label set heterogeneity and privacy constraints in federated learning.
method Apply classical classifier combination methods and adapt FL methods for private label sets, compare public and private settings.
result Reducing labels harms model performance, but centralized tuning can help.
This work improves fairness in federated learning by using zero-shot data augmentation.
problem Statistical heterogeneity leads to biased and less uniform accuracy across clients in federated learning.
method Proposes a federated learning system with zero-shot data augmentation to mitigate statistical heterogeneity and improve fairness.
result Empirical results show improved test accuracy and fairness across clients.
A federated learning framework using superquantile aggregation for robust performance across heterogeneous data.
problem Robust predictive performance across clients with heterogeneous data.
method Superquantile-based learning objective and stochastic training algorithm with differential privacy.
result Proves finite time convergence guarantees and demonstrates competitive performance with tail statistics improvement.
DFFL tackles federated learning with heterogeneous objectives and constraints.
problem Federated learning with clients having different objectives and feasible regions.
method Derived heterogeneity bounds for cost-vector distances and support-function/shape-distance terms. Lifted pointwise bounds to local-versus-federated excess-risk comparison.
result Federation is beneficial when the statistical advantage of pooling exceeds a client-specific heterogeneity penalty.
New method for decentralized learning from diverse clients.
problem Achieving high-performance global model from diverse client models.
method Decentralized Learning via Adaptive Distillation (DLAD).
result Demonstrates effectiveness of DLAD on multiple public datasets.
Client adaptation improves federated learning performance with non-IID data.
problem Improving model performance in federated learning with non-identically and non-independently distributed data.
method Simulates heterogeneous clients to learn client-specific conditioning using a conditional gated activation unit.
result Client adaptation enhances model performance across balanced and imbalanced data sets from audio and image domains.
FedForest adapts RF for federated learning, improving performance and efficiency.
problem Adapting RF for federated learning with heterogeneous data.
method FedForest uses a novel splitting procedure to aggregate client statistics, allowing non-parametric personalization.
result FedForest's federated RF achieves performance close to centralized models while being communication-efficient.
EM algorithm speeds up convergence in federated learning with heterogenous data.
problem Understanding convergence rates of federated learning algorithms under data heterogeneity.
method Characterized convergence rate of EM algorithm for FMLR model under various regimes.
result EM algorithm converges to ground truth with SNR ≥ √K in all regimes.
FLowDUP trains personalized models with unlabeled clients in low dimensions.
problem Training personalized models on clients with only unlabeled data.
method FLowDUP uses a forward pass with unlabeled data and a transductive PAC-Bayesian bound to generate personalized models in a low-dimensional subspace.
result FLowDUP enables efficient communication and computation with personalized models generated from unlabeled data.
FedLoRU improves FL efficiency by using low-rank updates.
problem Communication inefficiency and performance reduction in Federated Learning.
method Proposes FedLoRU, a low-rank update framework for FL, which reduces communication costs while maintaining performance.
result FedLoRU achieves convergence rates similar to FedAvg and is robust to heterogeneous and large numbers of clients.
A new Federated Learning approach balances personalization and global training.
problem Breaking the curse of data heterogeneity in Federated Learning.
method Splitting variables into global and local parameters, using a simple algorithm.
result The approach allows each client to fit their data perfectly, breaking the curse of data heterogeneity.
SCAFFLSA reduces communication complexity for federated learning with heterogeneous clients.
problem Quantifying and reducing communication complexity in federated learning with heterogeneous clients.
method Proposes SCAFFLSA, a variant of FedLSA using control variates to correct for client drift.
result SCAFFLSA achieves logarithmic communication complexity for statistically heterogeneous agents, scaling with the inverse of the desired accuracy.
Federated learning framework improves model generalization and privacy.
problem Communication overhead and statistical heterogeneity in FL.
method Prototypes and lightweight adapters for local model refinement.
result Improves classification accuracy over baseline algorithms.
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that exists in both resource and data due to the differences in computation and communication capacity, as well as the quantity and content of data…
Theoretical study explains why federated optimization fails to achieve perfect fitting.
problem Performance degradation in federated optimization under data heterogeneity.
method Assumption of distinct local optima due to client data heterogeneity.
result The global objective has a lower bound that prevents perfect fitting of all client data.
A new federated algorithm reduces regret in X-armed bandit problems.
problem Collaborative optimization of heterogeneous local objectives.
method Fed-PNE algorithm using hierarchical partitioning and weak smoothness.
result Achieves sublinear cumulative regret with minimal communication.
Federated Averaging (FedAvg) has emerged as the algorithm of choice for federated learning due to its simplicity and low communication cost. However, in spite of recent research efforts, its performance is not fully understood. We obtain tight convergence rates for FedAvg and prove that it suffers from `client-drift' w…
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
problem Byzantine attacks and communication inefficiency in federated learning.
method Transformed robust aggregation for zero-order optimization under client heterogeneity.
result CyBeR-0 achieves stable performance with minimal communication costs and reduced memory usage.
This paper surveys techniques to personalize federated learning models.
problem Personalized models outperform shared models for some clients, reducing participation.
method Surveys recent research on personalizing federated learning models.
result Personalization techniques improve model performance for individual clients.
A new federated learning method speeds up training by selecting faster nodes first.
problem System heterogeneity and stragglers slow down federated learning.
method Adaptive selection of nodes based on data statistical characteristics.
result Significant speedup in wall-clock time compared to standard federated learning.
This paper optimizes brokerage contracts for multiple clients trading a single asset.
problem Optimizing brokerage contracts for multiple clients trading a single asset.
method Endogenously determines clients' reservation values and strategically chooses clients. Characterizes optimal portfolios computationally.
result Characterizes optimal portfolios of clients and their profits, showing dependence on price impact coefficients.
Byzantine-resilient federated learning with local iterations and robust mean estimation.
problem Byzantine clients disrupt federated learning with local iterations.
method Local SGD iterations, robust mean estimation, and matrix concentration result.
result Convergence analysis for strongly-convex and non-convex smooth objectives in heterogeneous data settings.
Paper proposes a robust method for federated ICA with geometric median aggregation.
problem Federated ICA with permutation ambiguity in client estimations.
method Geometric median aggregation with k-means clustering to resolve permutation ambiguity.
result The method provably remains effective in highly heterogeneous scenarios.
FedDuA adapts global learning rate for federated learning.
problem Slow convergence in federated learning due to dataset and parameter space heterogeneity.
method FedDuA uses mirror descent to adaptively select global learning rate based on inter-client and coordinate-wise heterogeneity.
result FedDuA achieves minimax optimal convergence for convex objectives and outperforms baselines in various settings.
A new method for online personalized learning reduces gradient variance by dynamically selecting peers.
problem Online personalized decentralized learning with statistically heterogeneous clients.
method Gradient-based collaboration criterion allowing clients to dynamically select peers with similar gradients.
result The method acts as a variance reduction method, achieving optimal performance in certain conditions.
Paper tackles RCA in complex networks with unknown interdependencies.
problem Difficult RCA in networked systems due to unknown interdependencies.
method Federated learning for feature-partitioned, nonlinear data without modifying client models.
result Established theoretical convergence guarantees and validated on real-world data.
This thesis tackles FL challenges with new methods and algorithms.
problem Privacy-preserving machine learning with decentralized data.
method Compression, client selection, and heterogeneity handling.
result Practical FL solutions with mathematically rigorous guarantees.
Paper proposes CLAIR for efficient LLM fine-tuning across clients.
problem Fine-tuning large language models (LLMs) efficiently and collaboratively.
method Federated LoRA fine-tuning with Collaborative Low-rank Alignment and Identifiable Recovery (CLAIR).
result CLAIR achieves better performance and contamination detection compared to local fine-tuning.
DRFLM improves federated learning by handling data heterogeneity and noise.
problem Data heterogeneity and noise in federated learning.
method Distributionally robust optimization and mixup techniques.
result Enhanced global model prediction accuracy through robust optimization and local mixup.
Personalized federated learning for diverse client objectives.
problem Training a single global model across diverse local datasets is not optimal.
method Efficiently calculates optimal weighted model combinations for each client based on their specific objectives.
result Our method outperforms existing alternatives and enables new personalized features.
Federated learning improves by training central model with client model outputs.
problem Direct averaging of client models is limited in FL.
method Ensemble distillation for model fusion.
result Central model trained faster with fewer communication rounds.
This paper analyzes and improves convergence in federated learning with biased client selection.
problem Analyzing convergence in federated learning with biased client selection.
method First convergence analysis of federated optimization for biased client selection strategies, proposing Power-of-Choice framework.
result Power-of-Choice strategies converge up to 3 times faster and give 10% higher test accuracy than random selection.
A collaborative algorithm reduces regret in federated linear contextual bandits.
problem Optimizing decision-making in federated learning with heterogeneous data.
method Fed-PE algorithm, leveraging geometric structure of rewards, multi-client G-optimal design.
result Achieves near-optimal regrets with logarithmic communication costs.