22 research outputs found
Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
The performance of clients in Federated Learning (FL) can vary due to various
reasons. Assessing the contributions of each client is crucial for client
selection and compensation. It is challenging because clients often have
non-independent and identically distributed (non-iid) data, leading to
potentially noisy or divergent updates. The risk of malicious clients amplifies
the challenge especially when there's no access to clients' local data or a
benchmark root dataset. In this paper, we introduce a novel method called Fair,
Robust, and Efficient Client Assessment (FRECA) for quantifying client
contributions in FL. FRECA employs a framework called FedTruth to estimate the
global model's ground truth update, balancing contributions from all clients
while filtering out impacts from malicious ones. This approach is robust
against Byzantine attacks and incorporates a Byzantine-resilient aggregation
algorithm. FRECA is also efficient, as it operates solely on local model
updates and requires no validation operations or datasets. Our experimental
results show that FRECA can accurately and efficiently quantify client
contributions in a robust manner
Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment
Learning from the collective knowledge of data dispersed across private
sources can provide neural networks with enhanced generalization capabilities.
Federated learning, a method for collaboratively training a machine learning
model across remote clients, achieves this by combining client models via the
orchestration of a central server. However, current approaches face two
critical limitations: i) they struggle to converge when client domains are
sufficiently different, and ii) current aggregation techniques produce an
identical global model for each client. In this work, we address these issues
by reformulating the typical federated learning setup: rather than learning a
single global model, we learn N models each optimized for a common objective.
To achieve this, we apply a weighted distance minimization to model parameters
shared in a peer-to-peer topology. The resulting framework, Iterative Parameter
Alignment, applies naturally to the cross-silo setting, and has the following
properties: (i) a unique solution for each participant, with the option to
globally converge each model in the federation, and (ii) an optional
early-stopping mechanism to elicit fairness among peers in collaborative
learning settings. These characteristics jointly provide a flexible new
framework for iteratively learning from peer models trained on disparate
datasets. We find that the technique achieves competitive results on a variety
of data partitions compared to state-of-the-art approaches. Further, we show
that the method is robust to divergent domains (i.e. disjoint classes across
peers) where existing approaches struggle.Comment: Published at IEEE Big Data 202
iDML: Incentivized Decentralized Machine Learning
With the rising emergence of decentralized and opportunistic approaches to
machine learning, end devices are increasingly tasked with training deep
learning models on-devices using crowd-sourced data that they collect
themselves. These approaches are desirable from a resource consumption
perspective and also from a privacy preservation perspective. When the devices
benefit directly from the trained models, the incentives are implicit -
contributing devices' resources are incentivized by the availability of the
higher-accuracy model that results from collaboration. However, explicit
incentive mechanisms must be provided when end-user devices are asked to
contribute their resources (e.g., computation, communication, and data) to a
task performed primarily for the benefit of others, e.g., training a model for
a task that a neighbor device needs but the device owner is uninterested in. In
this project, we propose a novel blockchain-based incentive mechanism for
completely decentralized and opportunistic learning architectures. We leverage
a smart contract not only for providing explicit incentives to end devices to
participate in decentralized learning but also to create a fully decentralized
mechanism to inspect and reflect on the behavior of the learning architecture
Fairness in Federated Learning via Core-Stability
Federated learning provides an effective paradigm to jointly optimize a model
benefited from rich distributed data while protecting data privacy.
Nonetheless, the heterogeneity nature of distributed data makes it challenging
to define and ensure fairness among local agents. For instance, it is
intuitively "unfair" for agents with data of high quality to sacrifice their
performance due to other agents with low quality data. Currently popular
egalitarian and weighted equity-based fairness measures suffer from the
aforementioned pitfall. In this work, we aim to formally represent this problem
and address these fairness issues using concepts from co-operative game theory
and social choice theory. We model the task of learning a shared predictor in
the federated setting as a fair public decision making problem, and then define
the notion of core-stable fairness: Given agents, there is no subset of
agents that can benefit significantly by forming a coalition among
themselves based on their utilities and (i.e., ). Core-stable predictors are robust to low quality local data from
some agents, and additionally they satisfy Proportionality and
Pareto-optimality, two well sought-after fairness and efficiency notions within
social choice. We then propose an efficient federated learning protocol CoreFed
to optimize a core stable predictor. CoreFed determines a core-stable predictor
when the loss functions of the agents are convex. CoreFed also determines
approximate core-stable predictors when the loss functions are not convex, like
smooth neural networks. We further show the existence of core-stable predictors
in more general settings using Kakutani's fixed point theorem. Finally, we
empirically validate our analysis on two real-world datasets, and we show that
CoreFed achieves higher core-stability fairness than FedAvg while having
similar accuracy.Comment: NeurIPS 2022; code:
https://openreview.net/attachment?id=lKULHf7oFDo&name=supplementary_materia