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.
FedSTaS stratifies and samples clients for efficient FL.
problem Inefficient client sampling in federated learning.
method Stratifies clients based on compressed gradients, uses Neyman allocation for sampling, and samples local data uniformly.
result FedSTaS achieves higher accuracy than FedSTS in fixed training rounds.
Federated learning is a recent advance in privacy protection. In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients. The resulting model is then distributed back to all clients, ultimately converging to a joint representative model without explicitly having to s…
New framework provides privacy guarantees for practical federated learning.
problem Inadequate privacy guarantees for federated learning due to restrictive assumptions.
method Fed- α \alpha α -NormEC, integrating multiple local updates, partial client participation, and standard assumptions. result Provably convergent and differentially private federated learning framework.
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.
UDJ-FL framework achieves multiple distributive justice-based fairness metrics in federated learning.
problem Ensuring fairness in federated learning across different client data distributions.
method UDJ-FL framework uses aleatoric uncertainty-based client weighing and fair resource allocation techniques.
result UDJ-FL achieves egalitarian, utilitarian, Rawls' difference principle, and desert-based fairness metrics.
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 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.
Paper addresses FL over MAC with DP constraints, proposing a novel consensus scheme.
problem Federated learning over a multiple access channel with differential privacy constraints.
method Proposes a novel consensus scheme using digital distributed stochastic gradient descent (D-DSGD) with artificial noise to preserve DP.
result Demonstrates improved convergence rate and DP level for a given MAC capacity.
Unified analysis of FL with arbitrary client participation.
problem Challenges of intermittent client availability and efficiency in FL.
method Introduces a generalized FedAvg and novel analysis capturing client participation.
result Unified convergence upper bounds for various participation patterns.
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.
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.
Federated learning can be vulnerable to adversarial attacks, which this work addresses.
problem Federated learning's adversarial vulnerability when deployed.
method Bias-Variance decomposition for federated learning, proposing Fed_BVA framework.
result Fed_BVA framework generates adversarial examples to improve robustness.
Privacy-preserving distributed deep learning method for multiple classification.
problem Privacy issues in training deep learning models for various fields.
method Split learning into common extractor, cloud model, and local classifier.
result Average performance improvement of 2.63% over existing local training models.
Federated Granger causality learns reliable interactions without sharing data.
problem Uncertainty in federated Granger causality estimates.
method Closed-form covariance recursions and spectral-radius-based convergence conditions.
result Uncertainty depends only on client data statistics and is independent of model parameters.
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.
Federated learning interprets temporal dynamics across clients with graph attention.
problem Interpreting temporal patterns across decentralized, heterogeneous systems with nonlinear dynamics.
method Graph Attention Network for learning state transition models over latent states communicated between clients.
result First interpretable characterization of cross-client temporal interdependencies in decentralized nonlinear systems.
A key factor in developing high performing machine learning models is the availability of sufficiently large datasets. This work is motivated by applications arising in Software as a Service (SaaS) companies where there exist numerous similar yet disjoint datasets from multiple client companies. To overcome the challen…
Detects backdoors in outsourced models by replicating training steps across multiple servers.
problem Detecting backdoors in models trained on cloud providers without prior knowledge.
method Replicate training steps across multiple servers to identify deviations and malicious updates.
result 99.6% accuracy in identifying backdoored models out of 50% malicious providers.
The paper analyzes XVA reduction strategies in financial crises using Mandatory Breaks, Restructuring, and Resets.
problem Challenges in client XVA management during crises when continuous collateralization is not feasible.
method Compares multiple trade strategies including Mandatory Breaks, Restructuring, and Resets.
result Resets can be twice as effective as Mandatory Breaks/Restructuring if there is no credit recovery. When recovery is at least 1/3, Mandatory Breaks/Restructuring can be more effective.
Locally adaptive federated learning improves convergence in distributed machine learning.
problem Balancing local updates in federated learning leads to slow convergence.
method Locally adaptive federated learning algorithms that use uncoordinated stepsizes based on local geometric information.
result Locally adaptive methods can be particularly efficient in overparameterized settings and outperform standard federated algorithms.
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 ) \mathcal{O}(ε^{-1.5}) O ( ε − 1.5 ) convergence to ε ε ε -stationary point, compared to O ( ε − 2 ) \mathcal{O}(ε^{-2}) O ( ε − 2 ) . pFedGP uses Gaussian processes for personalized federated learning with improved kernel learning.
problem Learning personalized models across clients with limited data.
method pFedGP uses Gaussian processes with deep kernel learning, including a shared neural network kernel and inducing points.
result pFedGP achieves well-calibrated predictions and significantly outperforms baseline methods.
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.
Synthetic data replaces model updates in federated learning.
problem High computational costs in transmitting model parameters.
method Transmitting synthetic data instead of model updates.
result Reduces communication costs by more than an order of magnitude.
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.
Federated learning method selects relevant data to improve model accuracy.
problem Noisy and irrelevant data negatively impacts model training in federated learning.
method Use a benchmark model to evaluate and select relevant data subsets from each client.
result Up to 25% improvement in model accuracy compared to training with all data.
This paper addresses privacy in federated learning with wireless clients and base stations.
problem Privacy of clients' data in federated learning with hierarchical wireless architecture.
method Derives communication cost limits and introduces private aggregation schemes tailored for hierarchical wireless systems.
result Private aggregation schemes reduce communication costs by multiplicative factors compared to information-theoretic limits.
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.
New algorithm tackles multi-agent bandits with heavy-tailed data.
problem Maximizing system performance in multi-agent settings with heavy-tailed data.
method Algorithm exploits hub-like structures and synchronization among clients.
result Regret bound of O ( M 1 − 1 α log T ) O(M^{1 -\frac{1}α} \log{T}) O ( M 1 − α 1 log T ) for homogeneous settings, O ( M log T ) O(M \log{T}) O ( M log T ) for heterogeneous. 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.
Survey classifies Clustered Federated Learning into three types of approaches.
problem Non-independent and identically distributed (non-IID) data in Federated Learning.
method Systematic review of CFL literature, principled taxonomy.
result Core CFL and Metadata-based approaches have distinct focuses.
SureMap estimates model performance across subpopulations efficiently.
problem Estimating model performance across subpopulations with scarce data.
method Simultaneous Gaussian mean estimation with external data.
result High accuracy in both multi-task and single-task disaggregated evaluations.
A novel algorithm reduces communication costs in federated best arm identification.
problem Identifying the best arm in a federated multi-armed bandit setup with minimal communication cost.
method Proposes a novel algorithm called FedElim that communicates only in exponential time steps.
result Demonstrates that communication is almost cost-free in FedElim, with a total cost at most 3 times the maximum under its variant.
FedAvg converges linearly to global minimum in federated learning with partial participation.
problem Challenges in federated learning with partial client participation.
method Federated averaging (FedAvg) method for over-parameterized neural networks.
result FedAvg converges to global minimum at a linear rate after t iterations.
Clust-PSI-PFL uses PSI to improve accuracy and fairness in federated learning.
problem Non-IID data biases federated learning performance.
method Clust-PSI-PFL uses clustering and PSI to form homogeneous groups of clients.
result Clust-PSI-PFL delivers up to 18% higher global accuracy and improves client fairness.
Paper tackles unknown participation in FL, proposing FedAU for better performance.
problem Unknown participation statistics in federated learning impact performance.
method Adapting aggregation weights in FedAvg based on participation history.
result FedAU converges to optimal solution and has desirable properties.
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.
New bounds on generalization error for distributed learning using rate-distortion theory.
problem Establishing upper bounds on generalization error for distributed learning algorithms.
method Using rate-distortion theory, the paper introduces new bounds that depend on the compressibility of each client's algorithm.
result The bounds suggest that the generalization error of the distributed setting decays faster than that of the centralized one with a factor of O ( log ( K ) / K ) \mathcal{O}(\log(K)/\sqrt{K}) O ( log ( K ) / K ) . Bank behaviour is important for pricing XVA because it links different counterparties and thus breaks the usual XVA pricing assumption of counterparty independence. Consider a typical case of a bank hedging a client trade via a CCP. On client default the hedge (effects) will be removed (rebalanced). On the other hand, …
The paper analyzes RfQ processes on MD2C platforms using probabilistic models.
problem Analyzing the negotiation process on MD2C platforms to ensure dealers' profitability.
method Probabilistic graphical models and causal inference.
result Generative models can match predictive accuracy of discriminative models while enforcing business requirements.
Quantum federated learning improves with non-IID data using one-shot communication.
problem Performance degradation in federated learning with non-IID data.
method Quantum algorithms and local density estimators for non-IID data.
result One-shot communication complexity for non-IID quantum federated learning.
GC-FCP provides efficient federated CP with group-conditional coverage guarantees.
problem Uncertainty quantification in federated settings with distributed calibration data.
method Group-conditional federated conformal prediction (GC-FCP) using group-stratified coresets.
result GC-FCP offers efficient aggregation and calibration compared to centralized methods.
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.
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.
Federated Learning allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their local data to a centralized server. This form of privacy-preserving collaborative learning however comes at the cost of a significant communication overhead du…
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.
Model analyzes OTC market making with reputation feedback.
problem Optimizing electronic OTC liquidity provision considering reputation.
method Developed a stochastic-control model with feedback loops.
result Policy alternates between reputation-building and franchise monetization phases.