New bucketing scheme improves Byzantine robustness for heterogeneous data.
problem Byzantine attacks on federated learning with heterogeneous data.
method Bucketing scheme to adapt robust algorithms to non-iid data.
result Bucketing scheme ensures convergence against Byzantine attacks.
New methods improve Byzantine robustness in distributed learning.
problem Existing robust aggregation rules fail in realistic scenarios.
method Introducing new robust iterative clipping procedure and worker momentum.
result First provably robust method for standard stochastic optimization.
New findings show privacy affects generalization error in a non-monotonic way.
problem Privacy and robustness in distributed learning.
method Theoretical analysis and matching lower/upper bounds on algorithmic stability.
result Generalization error is non-monotonically affected by privacy, depending on noise level.
Paper proposes a secure protocol for federated learning.
problem Combining robustness, privacy, and security in federated learning.
method Secure two-server protocol for federated learning.
result Offers both input privacy and Byzantine-robustness.
New algorithm identifies near-optimal policies in adversarial distributed RL settings.
problem Adversarial agents in distributed RL settings that can collude and report arbitrary data.
method Weighted-Clique algorithm for robust mean estimation from batches, combined with novel distributed algorithms.
result Achieves superior robustness guarantees and near-optimal sample complexities in both offline and online settings.
New federated learning protocols resist Byzantine failures and offer privacy guarantees.
problem Resisting Byzantine failures in federated learning.
method Proposes robust federated learning protocols with optimal statistical rates and privacy guarantees.
result Achieves nearly optimal statistical rates and tight rate in terms of all parameters for strongly convex losses.
New algorithm improves decentralized learning in the presence of Byzantine faults.
problem Byzantine faults in decentralized learning on arbitrary graphs.
method Proposes ClippedGossip for Byzantine-robust consensus and optimization.
result First to provably converge to a specified neighborhood of the stationary point for non-convex objectives.
A new method balances model quality and Byzantine robustness in Federated Learning.
problem Byzantine clients sending arbitrary or malicious information.
method Practical weight-truncation-based preprocessing method.
result Empirically demonstrates good balance between model quality and Byzantine robustness.
Paper improves statistical efficiency of median-of-means estimator for Byzantine robust distributed inference.
problem Byzantine robustness in distributed learning systems.
method Variance reduced median-of-means (VRMOM) estimator for Byzantine robust distributed inference.
result Achieves a fast convergence rate with only a constant number of rounds of communications.
Unified framework for Byzantine robust gossip algorithms with guaranteed performance.
problem Vulnerability of decentralized machine learning to misbehaving devices.
method Introduces F-RG framework and CS+ robust aggregation rule for Byzantine resilience.
result CS+-RG has near-optimal breakdown tolerance and outperforms existing methods.
New method improves model accuracy in Byzantine-robust distributed learning by optimizing batch size.
problem Reduces model accuracy drop due to large variance of stochastic gradients in Byzantine-robust distributed learning.
method Proposes ByzSGDnm, a novel BRDL method that uses normalized momentum to mitigate accuracy drop in large batch sizes.
result The optimal batch size increases with the fraction of Byzantine workers, leading to better model accuracy under Byzantine attacks.
DynBRO learns robustly from dynamic Byzantine workers.
problem Fault-tolerant distributed learning with dynamic Byzantine workers.
method Multi-level Monte Carlo (MLMC) gradient estimation and adaptive learning rate.
result DynaBRO nearly matches static setting's convergence rate with O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) Byzantine worker changes. Paper develops Byzantine-resilient algorithms for decentralized learning.
problem Vulnerability of distributed learning to Byzantine attacks.
method Dual approach for decentralized optimization.
result Convergence guarantees and experimental validation of the proposed algorithm.
Proposes a method to improve Byzantine-robustness in compressed federated learning.
problem Byzantine-robustness in compressed federated learning.
method Gradient difference compression and stochastic average gradient algorithm (SAGA).
result The proposed method reaches a neighborhood of the optimal solution at a linear convergence rate.
Paper develops efficient algorithms for robust distributed learning with statistical guarantees.
problem Limited communication power and adversarial node behaviors in distributed learning.
method Surrogate likelihood framework and median/trimmed mean operations.
result Provable robustness against Byzantine failures and optimal statistical rates.
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.
Paper tackles Byzantine attacks in distributed learning with a new ADMM method.
problem Byzantine workers sending arbitrary messages bias distributed learning.
method Byzantine-robust stochastic ADMM exploiting separable problem structure.
result Proposed method converges to optimal solution at O(1/k) rate.
Paper addresses Byzantine attacks in decentralized optimization over networks.
problem Byzantine attacks in decentralized stochastic optimization over static and time-varying networks.
method Formulate a TV norm-penalized approximation of the problem, solve using stochastic subgradient method.
result Proposed method reaches a neighborhood of the Byzantine-free optimal solution.
Efficient distributed learning with Byzantine-resilient thresholding and error feedback.
problem Byzantine-resilient distributed learning with communication efficiency.
method Simple thresholding for Byzantine mitigation, compressed gradients and norms for aggregation, error feedback.
result Statistical error rate matches Yin et al.~\cite{dong} but with simpler schemes, and improved convergence with error feedback.
Paper tackles Byzantine attacks in Federated Learning by clustering and robustifying.
problem Adversarial attacks from Byzantine machines in Federated Learning.
method Iterative Federated Clustering Algorithm (IFCA) with trimmed mean and median aggregation.
result Improved convergence rate for strongly convex loss functions in Byzantine-Robust IFCA.
A method to robustly federate learning with non-i.i.d. data and Byzantine workers.
problem Byzantine workers sending malicious messages in federated learning with non-i.i.d. data.
method Resampling strategy to reduce inner and outer variation, stochastic average gradient, robust geometric median aggregation.
result The method reaches a neighborhood of the optimal solution at a linear convergence rate and learning error depends on the number of Byzantine workers.
We study robust distributed learning that involves minimizing a non-convex loss function with saddle points. We consider the Byzantine setting where some worker machines have abnormal or even arbitrary and adversarial behavior. In this setting, the Byzantine machines may create fake local minima near a saddle point tha…
Paper addresses robust federated linear bandits against Byzantine attacks.
problem Byzantine attacks on a small fraction of agents in federated learning.
method Proposes a geometric median-based robust aggregation oracle.
result Achieves sublinear regret bound of i l d e O ( T 3 / 4 ) ilde{\mathcal{O}}({T^{3/4}}) i l d e O ( T 3/4 ) robust to fewer than half Byzantine agents. In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures -- arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithm…
Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learning techniques are vulnerable to Byzantine failures, biased local datasets, and poisoning attacks. In this paper we introduce Adaptive Federa…
We study a recently proposed large-scale distributed learning paradigm, namely Federated Learning, where the worker machines are end users' own devices. Statistical and computational challenges arise in Federated Learning particularly in the presence of heterogeneous data distribution (i.e., data points on different de…