A specific inference concerning the convergence of Federated learning.

Introduction

This is a note to record the inference steps of the FedAvg convergence. Similar to Model concergence analysis we conduct convergence anlysis in FedAVG, and it is originated from [[Wang 等。 - 2021 - A Field Guide to Federated Optimization.pdf]] .

Model convergence Assumptions and Preliminaries

1. Local update SGD: \(x_i\) is the local model parameter for client $i$. t: global round index; k: local step index \(x_i^{(t,k+1)}:= x_i^{(t,k)}-\eta\frac{\partial \mathcal{L}}{\partial x_i^{(t,k)}} = x_i^{(t,k)}-\eta\nabla x_i^{(t,k)}\)

2. Overall optimization objective: We have client population as $\mathcal{C} = {1,2,3,\cdots,M}$ the objective is \(\min F(x)=\mathbb{E}_{i\sim\mathcal{C}}(F_i(x))\)

Properties of local objectives

1. Each local objective $F_i(x)$ is convex abnd L-smooth. Each client can query an unbiased stochastic gradient with σ2-uniformly bounded variance in L2 norm.
2. Poperty1: Unbiased stocahstic gradient: expectation of the stochastic gradient for a given $x_i^{(t,k)}$ is equal to the averaged local gradient for a given model $\phi(\cdot)$ This is to say, the gradient expectation of the SGD equals to the gradient of the GD (Unbiased stochastic gradient)[[Stochastic Gradient Descent vs Gradient Descent]] \(\mathbb{E}[\nabla x_i^{(t,k)}|x_i^{(t,k)}]=\nabla F_i(x_i^{(t,k)})\)Given a dataset of $D_i = {(\mathcal{x}i,\mathcal{y}_i)}{i=0}^N$ , where the N denotes the length of the whole dataset. The objective of the GD and its gradients are calculated as:

In this case, the expectation is the weighted average of a single batch with batchsize as bn, i.e.,

\(\begin{align} \mathbb{E}\left[\nabla x_i^{(t,k)}|x_i^{(t,k)}\right] &= \sum^{N-bn+1}_{j=1} \left(\sum_{i=1}^{bn}{\frac{\partial \mathcal{L}}{\partial x_{i,s_j}^{(t,k)}}}\cdot P(I=i|S=s_j)\right)\cdot P(S=s_j) \\ &= \sum^{N-bn+1}_{j=1} \left( P(I=i|S=s_j)P(S=s_j)\sum_{i=1}^{bn}{\frac{\partial \mathcal{L}}{\partial x_{i,s_j}^{(t,k)}}} \right)\\ & = \frac{1}{N}\sum_{i=1}^N{\frac{\partial \mathcal{L}}{\partial x_{i}^{(t,k)}}}\\ &= \frac{1}{N}\sum_{i=1}^{N}\nabla\mathcal{L}(\phi(\mathcal{x}_i),\mathcal{y}_i) \\ & = \nabla F_i(x_i^{(t,k)}) \end{align} \tag{1}\) where batch set is $S = {s_1,\cdots,s_{bn}}$.Note: SGD or Adam is a stochastic optimzation algorithm that select the samples from the batch randomly for gradient calculation.

1. Property 2: Bounded variance: \(\mathbb{E}\left[\left\Vert\nabla x_i^{(t,k)}-\nabla F_i(x_i^{(t,k)})\right\Vert^2|x_i^{(t,k)}\right] \leq \sigma^2\) This is to say, the gradient of the SGD is close to the gradient of the GD

Data heterogeneity/similarity across clients

Local gradient $\nabla F_i(x)$ and global gradient $\nabla F(x)$ is $\zeta$ - uniformly bounded.
\(\max_l{\sup_x{\left\Vert \nabla F_i(x_i^{(t,k)})-\nabla F(x_i^{(t,k)})\right\Vert}} \leq \zeta\)

How do we measure the convergence of the FedAVG?

Generally, we want to prove that \(\|F(x^{(t,k+1)})-F(x^*)\|\leq\|F(x^{(t,k)})-F(x^*)\| , \forall t,k \in [1,2,3,\cdots]\) where $F(x^*)$ is the optimal. Or, we give a weaker claim: \(\mathbb{E}\left[\frac{1}{\tau T} \sum_{t=0}^{T-1} \sum_{k=1}^\tau F\left(\overline{\boldsymbol{x}}^{(t, k)}\right)-F\left(\boldsymbol{x}^{\star}\right)\right] \leq \text{ an upper bound decreasing with } T \text {. }\)

Note that $\mathbb{E}$ in this paper denotes $\mathbb{E}_{i\sim \mathcal{C}}$ , where $\mathcal{C}$ denotes the client set. Therefore, we can say that the $\mathbb{E}$ is generally calculating the expectation over all the clients.

Decentralized optimization: Originated from the decentralized optimization, we derive the shadow sequence to indicate the uodate process.

\(\overline{x}^{(t,k)}:=\frac{1}{M}\sum^{M}_{i=1}{x_i^{(t,k)}}\) Then, at round $t$ local epoch $k+1$, \(\overline{x}^{(t,k+1)}= \overline{x}^{(t,k)}-\eta \frac{1}{M}\sum^{M}_{i=1}{x_i^{(t,k)}}\)

Two lemmas to facilitate the convergence proof

This paper want to prove the Theorem as follows. \(\mathbb{E}\left[\frac{1}{\tau T} \sum_{t=0}^{T-1} \sum_{k=1}^\tau F\left(\overline{\boldsymbol{x}}^{(t, k)}\right)-F\left(\boldsymbol{x}^{\star}\right)\right] \leq \text { an upper bound decreasing with } T \text {. }\)

Lemma1 the paper proves that the expectation for each round is bounded. \(\mathbb{E}\left[\frac{1}{\tau} \sum_{k=1}^\tau F\left(\overline{\boldsymbol{x}}^{(t, k)}\right)-F\left(\boldsymbol{x}^{\star}\right)\bigg|\mathcal{F}^{(t,0)}\right] \leq Bound\)

Lemma2 the paper proves that the Bound in the lemma 1 is decreasing with T. Specific prove is available in Paper

Why this paper evaluate the expection instead of loss decrease in each step?

Our guess is that each-step loss may not decrease with T, but its expectation is bounded. Lets prove it in the following paragraphs.

Prove of the step-by-step decrease in loss function

We want to prove the Theorem that \(\|F(x^{(t,k+1)})-F(x^*)\|\leq\|F(x^{(t,k)})-F(x^*)\| , \forall t,k \in [1,2,3,\cdots]\) we spilt into two therom